BRT Before and After Study - University of Utahmrc.cap.utah.edu/wp-content/uploads/sites/8/2015/...Provo/Orem BRT Before and After Study: Initial Conditions Report Matthew M. Miller,

Provo/OremBRT

BeforeandAfterStudy:

InitialConditionsReport

MatthewM.Miller,

MercedesBeaudoin,

andReidEwing

UniversityofUtah,

MetropolitanResearchCenter

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______________________________________________________________________________ Provo/Orem BRT Before and After Study: Initial Conditions Report

Report No. UT‐17.XX

PROVO-OREM TRANSPORTATION IMPROVEMENT PROJECT (TRIP)

Prepared for:

Utah Department of Transportation

Research Division

Submitted by:

University of Utah,

Metropolitan Research Center

Authored by:

Matthew M. Miller, Mercedes Beaudoin,

and Reid Ewing

Final Report

June 2017

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DISCLAIMER

The authors alone are responsible for the preparation and accuracy of the information,

data, analysis, discussions, recommendations, and conclusions presented herein. The contents

do not necessarily reflect the views, opinions, endorsements, or policies of the Utah

Department of Transportation or the U.S. Department of Transportation. The Utah Department

of Transportation makes no representation or warranty of any kind, and assumes no liability

therefore.

ACKNOWLEDGMENTS

The authors acknowledge the Utah Department of Transportation (UDOT) for funding

this research through the Utah Transportation Research Advisory Council (UTRAC). We also

acknowledge the following individuals from UDOT for helping manage this research:

Jeff Harris

Eric Rasband

Brent Schvanaveldt

Jordan Backman

Gracious thanks to our paid peer reviewers in the Department of Civil & Environmental

Engineering, Brigham Young University:

Dr. Grant G. Schultz, Ph.D., P.E., PTOE.

Dr. Mitsuru Saito, Ph.D, P.E., F. ASCE, F. ITE

While not authors, the efforts of the following people helped make this report possible.

Data Collection Proof Reading/Edits

Ethan Clark Ray Debbie Weaver

Thomas Cushing Clint Simkins

Jack Egan Debolina Banerjee

Katherine A. Daly Katherine A. Daly

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Technical Report Documentation Page (Form DOT 1700.7)

1. Report No.

2. Government Accession No. 3. Recipients Accession No.

4. Title and Subtitle

Effects of Bus Rapid Transit on Traffic in a Travel Corridor: Provo/Orem BRT:

Before and After Study: Initial Conditions Report.

5. Report Date

2017‐06‐13 5:00:00 PM

6. Performing Organization Code

7. Author(s)

Matthew M. Miller, Mercedes Beaudoin, and Reid Ewing

8. Performing Organization Report No.

9. Performing Organization Name and Address

University of Utah, Metropolitan Research Center 375 S. 1530 E. Room 235 AAC Salt Lake City, Utah 84112

10. Work Unit No.

11. Contract (C) or Grant (G) No.

12. Sponsoring Organization Name and Address

Utah Department of Transportation, Research Division 4501 South 2700 West, PO Box 148410 Salt Lake City, UT 84114‐1265

13. Type of Report and Period Covered

Peer Reviewed Report; Final Report; 2015 14. Sponsoring Agency Code

15. Supplementary Notes

This project was funded by the Utah Department of Transportation (UDOT), the Utah Transit Authority (UTA),

Salt Lake County (SLCo), the Wasatch Front Regional Council (WFRC), and the Mountainland Association of

Governments (MAG).

16. Abstract (Limit: 200 words)

This report presents the initial conditions for the Provo‐Orem Transportation Improvement Project (TRIP). TRIP consists of a number of transportation improvements, including the construction of new Bus Rapid Transit. Traffic counts of Average Annual Daily Traffic were obtained for the project alignment and identified diversion corridors. Data on the ridership of all bus lines and rail lines within the study area was collected. To make it possible to establish trend lines for growth in traffic and in transit ridership, data from at least five years prior to BRT construction was collected for both AADT and transit ridership. To control for changes in traffic, it was necessary to control for traffic generation within the study area. County tax assessor data was used to determine initial levels of development within the study area, and these data were checked against aerial photo imagery. Each parcel in the assessor database was matched with the Institute of Transportation Engineers (ITE) Trip Generation Manual code it best resembles. Using the associated trip rate, trips generated for each parcel were estimated. The trips from all parcels in the study area were summed to estimate the total number of trips generated within the study area. For Brigham Young University and Utah Valley University, data on student enrollment and faculty and staffing levels were gathered to establish a baseline for travel demand.

17. Key Words

Bus Rapid Transit, Safety,

Environmental Quality,

Capacity, Congestion

18.Availability Statement

Metropolitan Research Center, University of Utah, Salt Lake City 84102 Phone: 801.587.9483 < http://plan.cap.utah.edu/research/metropolitan‐research‐center>.

19. Security Class (this report)

Unclassified

20. Security Class (this page) Unclassified

21. No. of Pages

142

22. Price NA

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Table of Contents 2

PROVO-OREM TRANSPORTATION IMPROVEMENT PROJECT (TRIP) ....................... 2

Technical Report Documentation Page .......................................................................................... 4

Table of Contents ............................................................................................................................ 5

Table of Figures ............................................................................................................................... 9

Acronyms & Abbreviations ........................................................................................................... 12

Executive Summary ....................................................................................................................... 14

1 Introduction ........................................................................................................................... 16

1.1 Study Overview .............................................................................................................. 16

1.2 Report Objectives ........................................................................................................... 17

1.3 Previous Research .......................................................................................................... 17

1.4 Report Structure ............................................................................................................. 17

1.4.1 Report Structure Outline ............................................................................................ 18

2 Project Context ...................................................................................................................... 19

2.1 Urban Context ................................................................................................................ 19

2.2 Transportation Context .................................................................................................. 19

3 Project Description ................................................................................................................ 21

3.1 Running Ways ................................................................................................................. 21

3.2 Stations & Stops ............................................................................................................. 21

3.3 Vehicles .......................................................................................................................... 21

3.4 Fare Collection ................................................................................................................ 22

3.5 Intelligent Transportation Systems ................................................................................ 22

3.6 Service and Operations Plans ......................................................................................... 24

3.7 Roadway Improvements ................................................................................................ 24

3.8 Active Transportation Improvements ............................................................................ 25

4 System Costs .......................................................................................................................... 26

4.1 Project Costs ................................................................................................................... 26

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4.2 Project Funding .............................................................................................................. 27

5 Planning, Design & Implementation ...................................................................................... 28

5.1 Institutional Context ....................................................................................................... 28

5.2 Project Development History ......................................................................................... 28

5.3 Project Design & Implementation .................................................................................. 30

5.4 Station & Guideway Planning ......................................................................................... 31

5.4.1 Station Changes Over Time ........................................................................................ 38

5.4.2 Guideway Changes Over Time .................................................................................... 38

5.4.3 Changes due to Campus Unification Plan................................................................... 39

6 Research Approach ................................................................................................................ 40

6.1 Quasi‐Experimental Research Design ............................................................................ 40

6.1.1 A Natural Experiment ................................................................................................. 40

6.1.2 Interrupted Time Series .............................................................................................. 40

6.2 Null Hypothesis Testing .................................................................................................. 41

6.3 Conceptual Framework .................................................................................................. 41

6.4 Data Collection Plan ....................................................................................................... 44

6.5 Study Bounds – Geographic Extents .............................................................................. 44

6.5.1 Diversion Corridors ..................................................................................................... 44

6.5.2 Future Alignment ........................................................................................................ 48

6.5.3 Buffered Alignment .................................................................................................... 49

6.6 Data Source Limitations ................................................................................................. 51

6.7 Timing of Traffic and Ridership Changes ........................................................................ 51

7 Evaluation of System Performance ....................................................................................... 53

7.1 Travel Times ................................................................................................................... 53

7.2 Schedule Reliability ........................................................................................................ 53

7.3 Identity & Image ............................................................................................................. 53

7.4 Safety & Security ............................................................................................................ 54

7.4.1 Safety .......................................................................................................................... 54

7.4.2 Security ....................................................................................................................... 55

7.5 Capacity .......................................................................................................................... 55

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8 Assessment of System Benefits ............................................................................................. 57

8.1 Transit Ridership............................................................................................................. 57

8.1.1 Methods & Data ......................................................................................................... 61

8.1.2 Results & Discussion ................................................................................................... 62

8.1.3 Data Limitations .......................................................................................................... 71

8.2 Capital Cost Effectiveness .............................................................................................. 73

8.3 Operating Cost Efficiency ............................................................................................... 74

8.4 Transit Supportive Land Use Development ................................................................... 74

8.4.1 Current Land Uses ...................................................................................................... 74

8.4.2 Potential Future Land Use .......................................................................................... 75

8.4.3 Timing of Land Use Changes ....................................................................................... 75

8.5 Environmental Quality (Vehicle Emissions) ................................................................... 78

8.5.1 Methods & Data ......................................................................................................... 78



9 System Effects on Roadway Network .................................................................................... 85

9.1 Traffic Counts ................................................................................................................. 85

9.1.1 Methods & Data ......................................................................................................... 85



9.2 Trip Generation by New Development .......................................................................... 96

9.2.1 Methods & Data ......................................................................................................... 96



9.3 New Transportation Infrastructure .............................................................................. 100

9.4 Student Enrollment & Employment ............................................................................. 102

9.4.1 Methods & Data ....................................................................................................... 102

9.4.2 Results & Discussion ................................................................................................. 103

9.4.3 Data Limitations ........................................................................................................ 106

9.5 Land Development ....................................................................................................... 106

9.5.1 Method & Data ......................................................................................................... 106

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9.6 Parking Supply & Occupancy ........................................................................................ 109

9.6.1 Methods & Data ....................................................................................................... 109



9.7 Crash Rates within Study Area ..................................................................................... 117

9.7.1 Methods & Data ....................................................................................................... 117


9.7.3 Data & Methodology Limitations ............................................................................. 119

10 Summary .......................................................................................................................... 120

PROVO-OREM TRANSPORTATION IMPROVEMENT PROJECT (TRIP) APPENDICES........................................................................................................................... 121

11 APPENDIX A. LITERATURE REVIEW .................................................................................. 122

11.1 Introduction .................................................................................................................. 122

11.2 Bus Rapid Transit and Safety ........................................................................................ 122

11.2.1 BRT & Traffic Crashes ............................................................................................... 122

11.2.2 BRT Literature Attempting to Measure Safety ......................................................... 123

11.2.3 BRT Design and Safety .............................................................................................. 125

11.3 Bibliography.................................................................................................................. 127

12 APPENDIX B.: ITE Trip Generation Rates .......................................................................... 129

13 APPENDIX C. Crash Locations .......................................................................................... 132

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Table of Figures

FIGURE 2‐1: PROJECT CONTEXT MAP ................................................................................................................................... 20

FIGURE 3‐1: LOCATION OF TRANSIT SIGNAL PRIORITY IMPROVEMENTS ....................................................................................... 23

FIGURE 4‐1: 2011 EA SYSTEM COSTS BY TRANSIT ELEMENT .................................................................................................... 26

FIGURE 4‐2: 2011 EA SYSTEM COSTS BY CATEGORY .............................................................................................................. 27

FIGURE 5‐1: 2007 MAG ALIGNMENT AND 2015 EA LOCALLY PREFERRED ALTERNATIVE .............................................................. 29

FIGURE 5‐2: JANUARY 2015 PROJECT TIMELINE .................................................................................................................... 30

FIGURE 5‐3: THE NOVEMBER 2016 PROJECT TIMELINE ........................................................................................................... 30

FIGURE 5‐4: 2010 UTA PROJECT INFO SHEET ....................................................................................................................... 32

FIGURE 5‐5: 2011 EA LOCALLY PREFERRED ALTERNATIVE ....................................................................................................... 33

FIGURE 5‐6: 2011 MAG 2040 PROJECT MAP ...................................................................................................................... 34

FIGURE 5‐7: 2013 PROJECT INFO SHEET .............................................................................................................................. 35

FIGURE 5‐8: 2014 EA LOCALLY PREFERRED ALTERNATIVE ....................................................................................................... 36

FIGURE 5‐9: DECEMBER 2016 PROJECT MAP ........................................................................................................................ 37

FIGURE 6‐1: CONCEPTUAL FRAMEWORK ............................................................................................................................... 43

FIGURE 6‐2: THE NOVEMBER 2016 PROJECT TIMELINE ........................................................................................................... 44

FIGURE 6‐3: MAP OF DIVERSION CORRIDORS AND SCREEN LINES .............................................................................................. 47

FIGURE 6‐4: MAP OF HOV EXCHANGE AT 800 SOUTH IN OREM ............................................................................................... 48

FIGURE 6‐5: MAP OF STUDY AREA DEVELOPMENT BOUNDARIES ............................................................................................... 50

FIGURE 7‐1: PEAK HOUR TRANSIT CAPACITY ......................................................................................................................... 55

FIGURE 8‐1: TRANSIT ROUTES NEAR UVU ............................................................................................................................ 58

FIGURE 8‐2: TRANSIT ROUTES AND HEADWAY IN STUDY AREA 2015 ......................................................................................... 59

FIGURE 8‐3: TRANSIT ROUTES IN STUDY AREA 2016 .............................................................................................................. 60

FIGURE 8‐4: TRANSIT ROUTES NEAR BYU 2015 .................................................................................................................... 61

FIGURE 8‐5: TRANSIT RIDERSHIP IN UTAH COUNTY ................................................................................................................. 62

FIGURE 8‐6: FRONTRUNNER RIDERSHIP 2009—2015 FOR OREM CENTRAL AND PROVO CENTRAL STATIONS ................................... 63

FIGURE 8‐7: UTAH COUNTY BUS RIDERSHIP BY MONTH 2010‐2015 ........................................................................................ 63

FIGURE 8‐8: ANNUAL AND DAILY RIDERSHIP FOR THE BRT ....................................................................................................... 64

FIGURE 8‐9: ROUTE 830, APRIL 2011 ................................................................................................................................. 65

FIGURE 8‐10: ESTIMATED AVERAGE DAILY RIDERSHIP, DRIVER COUNTS, ROUTE 830 ................................................................... 66

FIGURE 8‐11: ESTIMATED AVERAGE DAILY RIDERSHIP, APC, ROUTE 830 ................................................................................... 66

FIGURE 8‐12: FORECAST ROUTE RIDERSHIP, IN RIDERS/DAY ..................................................................................................... 67

FIGURE 8‐13: THE RYDE ROUTES 2016 ............................................................................................................................... 69

FIGURE 8‐14: THE RYDE ROUTES 2015 ............................................................................................................................... 70

FIGURE 8‐15: BOARDINGS AND ALIGHTINGS FOR ROUTES 830 AND 838 IN 2015 ........................................................................ 72

FIGURE 8‐16: EQUIVALENT ANNUAL COST CALCULATION ......................................................................................................... 73

FIGURE 8‐17: CAPITAL COST EFFICIENCY BASED ON 2011 FORECAST ......................................................................................... 74

FIGURE 8‐18: STUDY AREA LAND USES CIRCA 2015/2016 ..................................................................................................... 77

FIGURE 8‐19: VMT BY FACILITY TYPE AND GEOGRAPHY .......................................................................................................... 78

FIGURE 8‐20: SHARE OF VMT BY VEHICLE/FUEL TYPE AND GEOGRAPHY .................................................................................... 79

FIGURE 8‐21: EMISSIONS IN GRAMS PER MILE BY VEHICLE AND FUEL TYPE ................................................................................. 80

FIGURE 8‐22: SHARE OF EMISSIONS BY VEHICLE TYPE ............................................................................................................. 81

FIGURE 8‐23: AVERAGE ANNUAL EMISSIONS BY VEHICLE TYPE FOR DIVERSION CORRIDORS IN GRAMS ............................................. 82

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FIGURE 8‐24: SHARE OF VEHICLES BY VEHICLE AND FUEL TYPE ................................................................................................. 84

FIGURE 9‐1: INTERSECTIONS ALONG BRT ALIGNMENT ............................................................................................................. 86

FIGURE 9‐2: ESTIMATED AADT ON DIVERSION CORRIDORS 2010—2015 ................................................................................. 87

FIGURE 9‐3: ESTIMATED AADT BY DIVERSION CORRIDOR 2010‐2015 ...................................................................................... 88

FIGURE 9‐4: ESTIMATED AND FORECAST AADT 2010‐2020 ................................................................................................... 89

FIGURE 9‐5: ESTIMATED AADT BY SCREEN LINE 2010—2015 ................................................................................................ 90

FIGURE 9‐6: ESTIMATED AADT AND CHANGES BY SCREEN LINE 2010‐2015 .............................................................................. 91

FIGURE 9‐7: TRAFFIC SIGNAL COUNTS .................................................................................................................................. 91

FIGURE 9‐8: MAP OF DIVERSION CORRIDORS AND SCREEN LINES .............................................................................................. 94

FIGURE 9‐9: ‘BLOOD VESSEL’ MAP OF 2015 AADT ............................................................................................................... 95

FIGURE 9‐10: TRIP GENERATION TOTALS BY LAND USE CLASS .................................................................................................. 97

FIGURE 9‐11: MAP OF STUDY AREA LAND USE ...................................................................................................................... 99

FIGURE 9‐12: PROJECT AREA TRANSPORTATION IMPROVEMENTS ............................................................................................ 100

FIGURE 9‐13: STUDENT HEADCOUNT, FTE, AND FULL‐TIME STUDENTS FOR UVU ...................................................................... 103

FIGURE 9‐14: STUDENT HEADCOUNT, FTE, AND FULL‐TIME STUDENTS FOR BYU ...................................................................... 103

FIGURE 9‐15: STUDENT HEADCOUNT, FULL‐TIME EQUIVALENT, AND FULL‐TIME STUDENTS FOR BYU & UVU ................................ 104

FIGURE 9‐16: FULL‐TIME AND PART‐TIME EMPLOYMENT FOR UVU AND BYU 2010‐2015 ........................................................ 105

FIGURE 9‐17: FULL‐TIME AND PART‐TIME EMPLOYMENT FOR BYU ......................................................................................... 105

FIGURE 9‐18: FULL‐TIME AND PART‐TIME EMPLOYMENT FOR UVU ........................................................................................ 106

FIGURE 9‐19: STUDY AREA POPULATION & HOUSING UNITS PER ACRE .................................................................................... 107

FIGURE 9‐20: EXAMPLE OF ‘DOTTED’ PARKING STALLS .......................................................................................................... 109

FIGURE 9‐21: UVU PARKING MAP ................................................................................................................................... 110

FIGURE 9‐22: MAP OF PARKING AT BYU, 2015 .................................................................................................................. 111

FIGURE 9‐23: PARKING SUPPLY AND OCCUPANCY ................................................................................................................ 114

FIGURE 9‐24: PARKING OCCUPANCY PATTERN ..................................................................................................................... 115

FIGURE 9‐25: MAP OF ALIGNMENT AND DIVERSION CORRIDOR CRASH RATES ........................................................................... 118

FIGURE 11‐1: COMMON BRT CRASHES .............................................................................................................................. 123

FIGURE 11‐2: VECINO‐ORTIZE AND HYDER'S REVIEW OF BRT SAFETY STUDIES ........................................................................... 125

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Acronyms & Abbreviations

AA Alternatives Analysis

AADT Annual Average Daily Traffic

ACS American Community Survey

APC Automatic Passenger Counter

BRT Bus Rapid Transit

BYU Brigham Young University

CBD Central Business District

CH4 Methane

CNG Compressed Natural Gas

CO Carbon Monoxide

CO2 Carbon Dioxide

EA Environmental Assessment

EPA U.S. Environmental Protection Agency

FHWA Federal Highway Administration

FONSI Finding of No Significant Impact

FTA Federal Transit Administration

GREET Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation

HOV High Occupancy Vehicle

HOT High Occupancy Vehicle or Toll

I‐15 Interstate 15

IDTP Institute for Transportation and Development Policy

ITE Institute of Transportation Engineers

KML Keyhole Markup Language

LDS Church of Jesus Christ of Latter‐day Saints

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LBCS Land Based Classification System

LRTP Long Range Transportation Plan

LPA Locally Preferred Alternative

MAG Mountainland Association of Governments

MOVES Motor Vehicle Emissions Simulator

NOx Nitrous Oxides

NO2 Nitrous Dioxide

O3 Ozone

PER_DU Per Dwelling Unit

PER_KSF Per Thousand Square Feet

PM2.5 Particulate matter with a diameter of 2.5 microns or less

PM10 Particulate matter with a diameter of 10 microns or less parts per million

RFP Request For Proposal

SO2 Sulfur Dioxide

TIP Transportation Improvement Program

TOD Transit‐oriented Development

TRIP Transportation Improvement Project

TSP Transit Signal Priority

UDOT Utah Department of Transportation

UTA Utah Transit Authority

UVU Utah Valley University

VMT Vehicle Miles Traveled

VOC Volatile Organic Compounds

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Executive Summary

This report documents the initial conditions for the Provo‐Orem Transportation

Improvement Project (TRIP) alignment. The intent of the project is to analyze four outcomes: 1)

Traffic Counts; 2) Transit Ridership; 3) Land Development; and 4) Crash Rates while controlling

for changes in university enrollment, new trip generation from land development, and parking

supply. Collecting data on these factors prior to the improvements makes it possible to make

before and after comparisons, while controlling for other factors makes it possible to determine

the effects of the bus rapid transit (BRT). The default (“null”) hypothesis is that TRIP will have

no significant effects on any of the outcomes after controlling for other factors. This study uses

a quasi‐experimental design. A quasi‐experimental research design takes advantage of a

“treatment” applied to one group but not to another and compares the differences before and

after the treatment for both groups. In this case, the treatment consists of the improvements

included in the Provo‐Orem TRIP. To ensure the rigor of the analysis, the results are subject to

the critical review of independent experts who were not involved with the production of the

report.

Performing this quasi‐experimental analysis requires collecting data before and after the

treatment. In the case of this study, data for multiple years prior to the study were collected to

determine initial conditions for the initial conditions report. Following a gap year for the

construction period, data will be collected for three years of operations with a report issued for

each year, making an effective before and after comparison. Collecting this multiple‐year time

series makes it possible to project current trends into the future. In later reports, this will also

make it possible to compare projected trends to actual values. Initial conditions data collection

took place in 2015. After operations begin (circa 2019), a second round of data collection will

occur and a second report will be released. A third and fourth round of data collection are

planned for the following years.

For annual average daily traffic (AADT) counts on the BRT alignment and identified

diversion corridors, the trend line is flat for all corridors except Orem 820 North and I‐15.

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According to screen line counts, total traffic parallel corridors continue to grow. This suggests

that many of the corridors in the study area are at capacity and that traffic is already diverting

to alternate routes.

Overall, transit ridership is growing in Utah County. Much of that growth is FrontRunner

ridership. Total bus ridership was in decline before the advent of FrontRunner, but stabilized

thereafter, and is now rising. Ridership on Route 830 (the BRT alignment) has dropped

substantially since 2010. As a consequence, the forecasts from the 2011 Environmental

Assessment appear high. Possible reasons include changes in route alignment and changes in

BYU’s transit policy. The largest number of boardings for Route 830 take place at Orem

FrontRunner, Utah Valley University (UVU), the Timpanogas transit center, the southwest

corner of Brigham Young University (BYU), and Provo FrontRunner. Student enrollment and

staffing has recovered to its 2011 level, following a 2012‐2014 dip. UVU experienced a

substantial increase in enrollment and employees between 2008 and 2011, but has not

surpassed maximum ridership since.

All census blocks in the study area (a 1/4‐mile buffer around the alignment) were

queried to determine the number of housing units and population. This will be checked against

future census years, and will be used to gauge the effects of land development against current

population and housing units. All parcels within the study census blocks were assigned an ITE

trip generation code. Using data from the Utah County Assessor database, the number of

dwelling units and square footage of residential parcels was estimated to compare against

future conditions. Substantial additional data, such as the number of stories and other metrics,

was created using Google Earth. In future reports, changes in the Utah County Assessor

database can be checked against this data set to detect land use changes. The land uses within

the study area (6,219 acres) generate a very large number of trips, of which the majority can be

attributed to commercial uses.

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1 Introduction This section provides an overview of the study, detailing the purpose of the study, the history

and background of Bus Rapid Transit in America, the report objectives, and the structure of the

report.

1.1 Study Overview

This report investigates how the addition of bus rapid transit (BRT) affects roadway traffic using

a case study of a new BRT planned and designed for the cities of Orem and Provo in Utah

County, Utah. The project consists of 18 stations along a 10.5‐mile BRT alignment that connects

the Orem Intermodal Center with the Novell Provo Campus. Intermediate destinations include

Utah Valley University (UVU), Utah Valley Medical Center, University Mall, Brigham Young

University (BYU), the Provo Central Business District (CBD), the Provo Intermodal Center, and

Provo Town Center Mall.

Following the success of the Emerald Express BRT in Eugene, Oregon, many metropolitan areas

are making plans for BRT. At the same time, most cities are also experimenting with packages of

service and infrastructure improvements, frequently labeling these upgrades as BRT even

though few routes actually meet these criteria.

A 2011 report by the Institute for Transportation & Development Policy1 found that there are

only five bus corridors in the United States that meet the Bus Rapid Transit Standard: Eugene,

Las Vegas, Los Angeles, Cleveland, and Pittsburgh.2 Seattle’s SODO bus tunnel and San

Bernardino’s SBX met the BRT standard as of 2014.3 The Federal Transit Administration (FTA)

itself acknowledges two grades, BRT and BRT‐lite, with the former requiring 51 percent of the

alignment consisting of exclusive guide‐way. While BRT has long been suggested as a solution

to the urban transportation problem,4 the concept has only recently become popular in

American transportation policy. Cities across the nation are experimenting with ways to make a

“better bus.” Like other forms of rapid transit, BRT has been promoted as a means to reduce

congestion, improve air quality, reduce automobile dependence, and induce redevelopment.5

1 Weinstock, A., Hook, W., Replogle, M., & Cruz, R. (2011, May). Recapturing Global Leadership in Bus Rapid Transit: A Survey of Select U.S. Cities. Retrieved from https://www.itdp.org/wp‐content/uploads/2014/07/20110526ITDP_USBRT_Report‐LR.pdf 2 Institute for Transportation and Development Policy. (2014). The BRT Standard: 2014 Edition. Retrieved from https://www.itdp.org/wp‐content/uploads/2014/07/BRT‐Standard‐2014.pdf 3 Institute for Transportation and Development Policy. (2014). BRT Standard Scores. Retrieved from https://www.itdp.org/publication/brt‐standard‐scores/ 4 Meyer, J. R., Kain, J. F., & Wohl, M. (1966). The Urban transportation Problem. London: Harvard University Press. 5 Thole, C., & Samus, J. (2009). Bus Rapid Transit and Development: Policies and Practices that Affect Development Around Transit (No. FTA‐FL‐26‐7109.2009. 5).

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While there is a growing literature on BRT, studies are largely focused on ridership outcomes

and/or economic development. This study expands the scope of research to include the effects

of BRT on a number of other factors. This research is intended to provide generalizable

information about the effects of BRT.

1.2 Report Objectives

The objectives of this study are to determine the impacts of the Provo‐Orem BRT on both travel

demand along the corridor and the travel capacity of the corridor. The specific objectives are to

measure changes in:

Automobile traffic in the corridor between baseline and forecast conditions;

Land uses along the corridor;

Parking occupancy at the two universities;

Transit ridership;

Other co‐benefits of reduced automobile traffic, such as reduced vehicle emissions and

reduction in the number and severity of traffic collisions.

1.3 Previous Research

The analysis of the BRT project began with a review of academic and professional literature on

the effects of arterial BRT on traffic, congestion, and safety. The amount of literature available

proved to be extremely limited. Most before‐and‐after studies have focused on the effects on

transit ridership, travel‐time, reliability, and passenger satisfaction. Only four articles related to

BRT safety were found. The literature review can be found in APPENDIX A. LITERATURE

REVIEW.

1.4 Report Structure

This evaluation is organized in accordance with the framework outlined in the Characteristics of

Bus Rapid Transit for Decision‐Making6 report. This framework covers ridership, vehicle

emissions, and traffic crashes. This evaluation includes an additional sub‐section under

“Assessment of System Benefits” to address parking occupancy.

Additionally, the section “System Effects on Roadway Network” has been added to evaluate

other factors that we anticipate the Provo‐Orem Bus Rapid Transit will affect. These include the

6 Chang, M., Darido, G., Kim, E., Schneck, D., Hardy, M., Bunch, J., ... & Zimmerman, S. (2004). Characteristics of bus rapid transit for decision‐making (No. FTA‐VA‐26‐7222‐2004.1).

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effect of the BRT on automobile traffic volumes after controlling for new interchanges, land use

changes, university staffing and enrollment, and other factors.

1.4.1 ReportStructureOutline

Introduction

Project Context

Project Description

a. Running Ways

b. Stations/Stops

c. Vehicles

d. Fare Collection

e. Intelligent Transportation Systems (ITS)

f. Service and Operations

g. Marketing & Community Outreach

System Costs

Planning, Design & Implementation

Evaluation of System Performance

a. Travel Time

b. Reliability

c. Identity & Image

d. Safety & Security (Crashes)

e. Capacity

Assessment of System Benefits

a. Ridership

b. Capital Cost Effectiveness

c. Operating Cost Efficiency

d. Transit Supportive Land Development

e. Environmental Quality (Vehicle Emissions)

System Effects on Roadway Network

a. Traffic Counts and Trends

b. New Development

c. New Transportation Infrastructure

d. Student Enrollment

e. Parking Supply & Occupancy

f. Crash Rates

Conclusion

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2 Project Context This section covers the context for the project, both in terms of geographic location and in

terms of recent and ongoing transportation investments.

2.1 Urban Context

Utah County is the heartland of the state of Utah. It is currently experiencing rapid population

growth with an increase of 11.3 percent between 2010 and 2015.7 While the county is large,

the vast majority of the population is located in the linear corridor between Utah Lake and the

Wasatch Mountains. Most of the metropolitan population is distributed along Interstate‐15 (I‐

15), the major limited‐access highway in the county. Other major highways include Highway 89

and Highway 189. The contiguous urbanized area (by incorporated boundaries) now stretches

from the edge of Salt Lake County south to Spanish Fork, and westward from the Wasatch

mountains to the cities of Eagle Mountain and Fairfield. The urbanized area is continuing to

expand, with additional cities incorporating and existing cities continuing to densify. Traffic

congestion has been a significant problem along I‐15 resulting in it being widened in 2014—

2016. Significant future travel demand is expected.

2.2 Transportation Context

Several major transit investments have recently been made in Utah County as part of the

strategy for meeting future travel demand caused by the expected population growth. With the

advent of FrontRunner Commuter Rail service in 2012, rapid transit returned to Utah County for

the first time in decades. The FrontRunner South project extended the existing FrontRunner

terminus from downtown Salt Lake City (in northern Salt Lake County) 44 miles south into the

middle of Utah County. As part of the extension, eight new stations were constructed, including

four in Utah County. As detailed in sub‐section 8.1, this extension resulted in a large increase in

total ridership on FrontRunner. Ridership at the Provo and Orem stations have increased each

year.

Like most modern commuter rails, FrontRunner re‐used a pre‐existing freight railway corridor,

which means that many of its stations are distant from major activity centers in the metro area.

The Provo‐Orem BRT helps solve this problem. It connects to both the Orem and the Provo

FrontRunner stations, and it provides a ‘last mile’ service connecting passengers using

7 United States Census Bureau. (n.d.). Quick Facts‐Utah County, Utah. Retrieved May 21, 2017, from

https://www.census.gov/quickfacts/table/PST120215/49049,00

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Frontrunner with major destinations within Utah County. Figure 2‐1 shows the location of the

planned BRT alignment, its connection to FrontRunner, and its relationship to major roadways.

Figure 2‐1: Project Context Map

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3 Project Description This section describes the characteristics of the Provo‐Orem Bus Rapid transit. Specifically, it

covers the running way, stations and stops, vehicles, fare collection, Intelligent Transportation

System elements, and the service and operation plan.

3.1 Running Ways

“The Project is approximately 10.5 miles long and consists of exclusive bus lanes (51 percent),

the majority of which are center lanes.” 8 The majority of the exclusive bus lanes are along

University Parkway between 400 West and US‐189. Much of the remainder is on US‐189

between Provo 700 North and Provo 300 south.

3.2 Stations & Stops

“The Project…consists of…18 stations, nine (9) center platforms, seven (7) side stations

consisting of 12 split side platforms, and two (2) intermodal centers.”9 The stations include

“pedestrian ramp improvements adjacent to proposed BRT stations to meet current Americans

with Disabilities Act (ADA) standards.” 10

3.3 Vehicles

According to the 2014 Findings of No Significant Impact (FONSI),11 the vehicles will have the

following characteristics:

The Project vehicles will be articulated buses (i.e., two rigid buses linked together by a

pivoting joint in the middle) capable of holding 200 persons each. Since most stations

are planned as center platforms, vehicles will provide two‐sided boarding, with left‐side

and right‐side doors. Vehicles will be either hybrid‐electric or clean‐diesel powered; the

8 U.S. Department of Transportation Federal Transit Administration. (2015, March 27). Provo‐Orem Bus Rapid Transit Project. Retrieved from http://www.rideuta.com/uploads/ProvoOremBRTFONSI32715Finalwocomments.pdf 9 U.S. Department of Transportation Federal Transit Administration. (2015, March 27). Provo‐Orem Bus Rapid Transit Project. Retrieved from http://www.rideuta.com/uploads/ProvoOremBRTFONSI32715Finalwocomments.pdf 10 U.S. Department of Transportation Federal Transit Administration. (2015, March 27). Provo‐Orem Bus Rapid Transit Project. Retrieved from http://www.rideuta.com/uploads/ProvoOremBRTFONSI32715Finalwocomments.pdf 11 U.S. Department of Transportation Federal Transit Administration. (2015, March 27). Provo‐Orem Bus Rapid Transit Project. Retrieved from http://www.rideuta.com/uploads/ProvoOremBRTFONSI32715Finalwocomments.pdf

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type of vehicle will be determined during final design. Twenty‐five vehicles will be

needed to meet the frequency and length of the route, plus the spare ratio.

Two hundred persons is a very large number of passengers. Articulated buses can typically hold

twice as many persons as a regular bus. The New Flyer used for the EmX in Eugene holds about

90 passengers, and the Iris Civicbus used for the Las Vegas Max holds about 120 passengers.

3.4 Fare Collection

According to the 2014 FONSI,12 the project will use off‐board fare collection (using ticket

vending machines) for faster boarding. The Utah Transit Authority (UTA) already uses ticket

vending machines for TRAX light rail and FrontRunner commuter rail systems; presumably the

same software and hardware will be used. Electronic fare payment using ‘tap on/tap’ off cards

is also already in use by UTA, using magnetically‐stripped cards. While distance‐based fares

have been considered, there are no plans for them at this time.

3.5 Intelligent Transportation Systems

The system will use transit signal priority (TSP) at most signalized intersections; it will be a 15‐

second early/extend TSP.13 Figure 3‐114shows the intersection locations proposed to have

transit signal priority.

12 U.S. Department of Transportation, Federal Transit Administration. (2015, March 27). Provo‐Orem Bus Rapid Transit Project. Retrieved from http://www.rideuta.com/uploads/ProvoOremBRTFONSI32715Finalwocomments.pdf 13 U.S. Department of Transportation, Federal Transit Administration. (2015, March 27). Provo‐Orem Bus Rapid Transit Project. Retrieved from http://www.rideuta.com/uploads/ProvoOremBRTFONSI32715Finalwocomments.pdf 14 Adapted from Utah Transit Authority. (2014, December). Provo‐Orem Bus Rapid Transit Environmental Assessment: Executive Summary. Retrieved from http://www.rideuta.com/uploads/05_Provo_Orem_BRT_EA_Dec2014_ExecSummary.pdf

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Figure 3‐1: Location of Transit Signal Priority Improvements

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3.6 Service and Operations Plans

Modern articulated buses will operate at five‐minute headways during peak travel time.15

According the 2015 FONSI,16 peak periods are defined as the periods from 6:30 a.m. to 10:00

a.m. and from 3:30 p.m. to 6:00 p.m. Monday through Friday. The BRT system will operate

under the following plan:

Twelve buses per hour run in each direction with 5‐minute headways (6:30 a.m. to 10:00 a.m. and 3:30 p.m. to 6:00 p.m.).

Six buses per hour run in each direction with 10‐minute headways during mid‐day (10:00 p.m. to 3:30 p.m.).

Six buses per hour run in each direction with 10‐minute headways during early evening (6:30 p.m. to 8:00 p.m.).

Four buses per hour run in each direction with 15‐minute headways during early morning (4:30 a.m. to 6:30 a.m.) and late evening (8:00 p.m. to 11:00 p.m.).

Two buses per hour run in each direction with 30‐minute headways during late night (11:00 p.m. to 2:00 a.m.).

An existing maintenance depot located near the Orem Intermodal center will be used as a

maintenance center. According the 2015 FONSI: 17

In conjunction with the Project, but independent of the BRT lane and station

construction, UTA is expanding the existing Timpanogos Maintenance Facility located on

Geneva Road in Orem. The expansion of the Timpanogos facility will accommodate 25

additional 60‐foot articulated buses needed for the Project.

3.7 Roadway Improvements

The roadway elements of the project include adding a lane on University Parkway from 800

East to University Avenue, replacing the University Parkway Bridge over the Provo River, and

making intersection improvements at

University Parkway at 400 West,

University Parkway at Main Street,

15 Utah Transit Authority. (n.d.). Provo‐Orem Transportation Improvement Project. Retrieved May 17, 2017, from https://i4.rideuta.com/mc/?page=Projects‐Provo‐Orem‐Transportation‐Improvement‐Project 16U.S. Department of Transportation Federal Transit Administration. (2015, March 27). Provo‐Orem Bus Rapid Transit Project. Retrieved from http://www.rideuta.com/uploads/ProvoOremBRTFONSI32715Finalwocomments.pdf 17 U.S. Department of Transportation Federal Transit Administration. (2015, March 27). Provo‐Orem Bus Rapid Transit Project. Retrieved from http://www.rideuta.com/uploads/ProvoOremBRTFONSI32715Finalwocomments.pdf

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University Parkway at 200 East,

University Parkway at 800 East,

University Parkway at 2230 North,

University Parkway at Freedom Boulevard, and

University Avenue at 300 South. 18

3.8 Active Transportation Improvements

New and improved bicycle and pedestrian facilities through the corridor include:

New and/or wider sidewalks in many areas where sidewalks were narrow in width or in

disrepair;

Upgraded pedestrian ramps in locations where the current ramps do not meet the most

recent standards;

Wider pedestrian ramps at intersections along the College Connector Trail;

A bike lane in each direction along University Avenue from 400 South to 700 North

A safer gutter adjacent to the bike lanes along 700 North in Provo;

A median curb along 700 North directing pedestrians and bicyclists to cross only in

designated areas;

A crossing in Provo at 200 East that directs bicyclists into their own lane across 700

North;

Improvements to widen existing sidewalks along 900 East in Provo to 10‐foot‐wide

multi‐use paths; and

Increased width and height for the Provo River Trail where it crosses under University

Parkway. 19

18 Utah Transit Authority. (n.d.). Provo‐Orem Transportation Improvement Project. Retrieved May 17, 2017, from https://i4.rideuta.com/mc/?page=Projects‐Provo‐Orem‐Transportation‐Improvement‐Project 19 Utah Transit Authority. (n.d.). Provo‐Orem Transportation Improvement Project. Retrieved May 17, 2017, from https://i4.rideuta.com/mc/?page=Projects‐Provo‐Orem‐Transportation‐Improvement‐Project

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4 System Costs This section covers project cost and funding of the Provo‐Orem BRT.

4.1 Project Costs

The 2011 Environment Assessment capital costs for the project in 2013 dollars amounts to

$158.95 million.20 Figure 4‐1 shows the project elements by cost category. Figure 4‐2 provides a

breakdown of construction and non‐construction expenses.

Figure 4‐1: 2011 EA System Costs by Transit Element

20 Federal Transit Admininistration, Region VIII. (2011). Environmental Assessment, Provo‐Orem Rapid Transit Project, Utah County, Utah. Salt Lake City, Utah: Utah Transit Authority.

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Figure 4‐2: 2011 EA System Costs by Category

4.2 Project Funding

The Provo‐Orem BRT project is part of a larger project called Provo‐Orem TRIP. TRIP includes

related roadway and intersection improvements. These costs were not included in the FTA

grant. The TRIP project is funded from a variety of local, regional, state, and federal sources.

This includes $3 million of local sales tax, $7 million in donated leases of right of way in Provo

and Orem, $40 million state funding of UDOT improvements (on University Parkway), a $65

million local sales tax revenue bond, and $75 million from a federal transit grant. The total

project cost for TRIP is $190 million.21 The $40 million state funding UDOT improvements is part

of the project but not part of the FTA grant.22

21 Brown, K. (2016, March 4). What to Expect: The Provo‐Orem Transportation Improvement Project. [Blog post]. Retrieved May 17, 2017, from http://www.thechamber.org/blog/utah‐valley‐chamber‐221/post/what‐to‐expect‐the‐provo‐orem‐transportation‐improvement‐project‐2255 22 Federal Transit Administration. (n.d.). UT Provo‐Orem BRT Profile FY16. Retrieved May 17, 2017, from https://www.transit.dot.gov/sites/fta.dot.gov/files/docs/UT__Provo‐Orem_BRT_Profile_FY16.pdf

FTA SCC

NUMBERDescription Dollars

10 Guideway and track elements 30,762,000$

20 Statons, stops, terminals, intermodal centers 14,353,000$

30 Support facilities: yards, shops, buildings 1,996,000$

40 Site work and special Conditions 24,410,000$

50 Systems 10,910,000$

82,431,000$

60 Rights‐of‐way, land, existing improvements 3,797,000$

70 Vehicles 33,262,000$

80 Professional Services 18,532,000$

90 Unallocated Contingency 6,751,000$

100 Finance Changes 14,177,000$

158,950,000$

Construction Subtotal

Total

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5 Planning, Design & Implementation This section covers the planning, design, and the beginning of the construction of the Provo‐

Orem BRT.

5.1 Institutional Context

The Provo‐OREM BRT is part of a larger transportation improvement project known as the

Provo‐Orem Transportation Improvement Project (TRIP), which is a coordinated effort by

UDOT, UTA, Utah County, Mountainland Association of Governments (MAG), Provo City, and

Orem City to make improvements in the corridor. The project is located in Provo and Orem

Cities, which are part of the MAG Metropolitan Planning Organization. UTA is the transit

authority for the area. University Parkway is a state highway (SR 265).

5.2 Project Development History

The first published planning for a BRT in Utah County, the Inter‐Regional Corridor Alternatives

Analysis23 completed in 1999,24 suggested a BRT connecting the Provo and Orem FrontRunner

stations. The consulting firm Carter‐Burgess completed the Provo‐Orem Rapid Transit Feasibility

Study in 2005, and recommended an alignment along University Parkway and University

Avenue.25 In November of 2006, Utah County passed a ballot initiative to fund transportation.26

A BRT on University Avenue and University Parkway alignment was included in the 2007

regional transportation plan for MAG.27 Work on an Environmental Assessment began in 2007

and was completed in 2011.28 An Alternatives Analysis (AA) was completed as part of the

Environmental Assessment (EA) for the project. The Locally Preferred Alternative (LPA) from the

EA differed from the earlier MAG alignment; in the latter, the BRT wrapped around the eastern

edge of the BYU campus, along University Parkway, 900 East, and 800 North. Figure 5‐1

compares the two.

23 FrontRunner Commuter Rail: The Early Studies. (2006, June 13). Retrieved May 17, 2017, from http://utahrails.net/uta/uta‐frontrunner‐studies.php 24 Jackson, A. (n.d.). Utah.gov. Retrieved May 17, 2017, from https://www.utah.gov/pmn/files/145987.pdf 25 Utah Transit Authority. (2011, April 12). Provo‐Orem Bus Rapid Transit Environmental Assessment‐ Chapter 1. Retrieved from http://www.rideuta.com/uploads/06_Provo‐Orem_BRT_EA_April2011_Ch1_PN.pdf 26 2016 Transit Ballot Measures. (n.d.). Retrieved May 17, 2017, from http://www.cfte.org/elections/639/utah‐county 27 Utah Transit Authority. (2011, April 12). Provo‐Orem Bus Rapid Transit Environmental Assessment‐ Chapter 1. Retrieved from http://www.rideuta.com/uploads/06_Provo‐Orem_BRT_EA_April2011_Ch1_PN.pdf 28 Curtis, J. (2015, May 4). The Bus Rapid Transit Story. Retrieved May 17, 2017, from http://provomayor.com/2015/05/04/the‐bus‐rapid‐transit‐story/

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Figure 5‐1: 2007 MAG Alignment and 2015 EA Locally Preferred Alternative

In 2011, the LPA was included in the MAG 2040 Metropolitan Transportation Plan. In 2011 the

project received a FONSI from Federal Highway Administration (FHWA), making the project fully

eligible for Federal funding. Project Development for a Smart Starts grant began in April 2013,29

and the project was rated medium‐high by the FTA in 2014. The same year, the FTA asked UTA

to prepare another EA in response to changes in project description.30 Utah County funded the

EA and a 30 percent design plan about that time. In 2015, the project again received a medium‐

high rating for small starts. It was recommended for $71 million of funding in 2015.31 The

project received an additional FONSI on March 27, 2015.32 Full funding was approved by the

29 Federal Transit Administration. (n.d.). UT Provo‐Orem BRT Profile FY17. Retrieved May 17, 2017, from https://www.transit.dot.gov/sites/fta.dot.gov/files/docs/UT__Provo‐Orem_BRT_Profile_FY17_0.pdf 30 Pugmire, G. (2014, May 20). Provo Council hears timeline report on BRT. The Daily Herald. Retrieved May 17, 2017, from http://www.heraldextra.com/news/local/central/provo/provo‐council‐hears‐timeline‐report‐on‐brt/article_ef68cb55‐ba74‐5f27‐9b5f‐0bace00fe2a5.html 31 Federal Transit Administration. (n.d.). Proposed FY 2016 Funding for FTA Capital Investment Grant Program. Retrieved May 17, 2017, from http://www.apta.com/gap/legupdatealert/2015/Documents/New%20Starts%20list_FY16‐BudgetHighlights_USDOT.pdf 32 Federal Transit Administration. (2017, April 7). Environmental Decision Document. Retrieved May 17, 2017, from https://www.transit.dot.gov/regulations‐and‐guidance/environmental‐programs/environmental‐decision‐documents

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UTA board in July 2016,33 and in December of that year, the U.S. Department of Transportation

awarded the project a $75 million Small Starts grant.34

5.3 Project Design & Implementation

Initial design work for cost estimation was provided in 2013.35 A proposed preliminary design

map book was included as an appendix to the LPA in the 2014 EA.36 A Request for Proposals

(RFP) for design services was issued in January 2015.37 Figure 5‐2 shows that timeline.

Figure 5‐2: January 2015 Project Timeline

The current timeline is shown in Figure 5‐3; it shows final design being complete as of summer

2016, with the start of operations planned for early 2019.38

Figure 5‐3: The November 2016 Project Timeline

33 Lee, J. (2016, July 16). UTA approves funding for Provo‐Orem bus rapid transit system. KSL. Retrieved May 17, 2017, from https://www.ksl.com/?sid=40671034&nid=148 34Associated Press. (2016, December 20). $75M DOT grant to Utah Transit for new Provo‐Orem BRT line. Salt Lake Tribune. Retrieved May 17, 2017, from http://www.sltrib.com/home/4735641‐155/75m‐dot‐grant‐to‐utah‐transit 35Eliot, S. (2013, June 6). Proposed Utah county Transportation Taxes Project Selection Process. Retrieved May 17, 2017, from https://www.mountainland.org/img/minutes/Regional_Planning/2013/2013_06_06/Agenda%20Staff%20Reports.pdf 36 Federal Transit Administration & Utah Transit Authority. (2011, April 12). Provo‐Orem Bus Rapid Transit: Environmental Assessment‐ Appendix A Proposed Preliminary Design Map Book. Retrieved May 17, 2017, from http://www.rideuta.com/uploads/11_Provo_Orem_BRT_EA_Dec2014_AppdxA_Proposed_Design.pdf 37 Utah Legal Notices. (n.d.). Retrieved May 17, 2017, from http://www.utahlegals.com/notice.php?id=238305 38 Utah Transit Authority. (n.d.). Provo‐Orem Transportation Improvement Project: Project Overview. Retrieved May 17, 2017, from http://www.rideuta.com/About‐UTA/Active‐Projects/Provo‐Orem‐Transportation‐Improvement‐Project/Project‐Overview

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5.4 Station & Guideway Planning

In the LRTP, the project was envisioned as connecting two FrontRunner Stations, two

universities (UVU and BYU), and two malls (University Mall & Provo Town Center Mall). The

location of the two intermodal centers were anchored by the necessary proximity to

FrontRunner platforms. However, the number, location, and type of the platforms (center‐

median vs side) have changed over time. Changes to the stations and guideway over the length

of project development are shown in the following series of maps. An explanation of the

changes follows the maps.

The map for the 2010 UTA Project Info Sheet is shown in Figure 5‐4

The map for the 2011 EA LPA is shown in Figure 5‐5

The map for the 2011 MAG 2040 Project Map is shown in Figure 5‐6

The map for the 2013 Project Info Sheet is shown in Figure 5‐7

The map for the 2014 EA LPA is shown in Figure 5‐8

The map for the 2016 Project Map is shown in Figure 5‐9

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Figure 5‐4: 2010 UTA Project Info Sheet

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Figure 5‐5: 2011 EA Locally Preferred Alternative

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Figure 5‐6: 2011 MAG 2040 Project Map

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Figure 5‐7: 2013 Project Info Sheet

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Figure 5‐8: 2014 EA Locally Preferred Alternative39

39 Adapted from Utah Transit Authority. (2011, April 12). Provo‐Orem Bus Rapid Transit Environmental Assessment: Executive Summary. Retrieved from http://www.rideuta.com/uploads/05_Provo_Orem_BRT_EA_Dec2014_ExecSummary.pdf

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Figure 5‐9: December 2016 Project Map40

40 Utah Transit Authority. (n.d.). Provo‐Orem Transportation Improvement Project: Project Overview. Retrieved May 17, 2017, from http://www.rideuta.com/About‐UTA/Active‐Projects/Provo‐Orem‐Transportation‐Improvement‐Project/Project‐Overview

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5.4.1 StationChangesOverTime

This section details changes in the number, location, and type of stations along the alignment

over the course of project development. Figure 5‐4: 2010 UTA Project Info Sheet depicts two

intermodal stations, three stations near UVU, two on University Parkway (Orem 400 West and

Main Street), one at University Mall, one at University Parkway, one north of BYU, one east of

BYU, two South of BYU, four on University Avenue, one at Provo Town Center Mall, one north

of the East Bay Technology Park, and one south of it. Figure 5‐5: 2011 EA Locally Preferred

Alternative omits one station south of BYU and moves one of the stations on University

Boulevard from Provo 300 South (US‐89) to 400 South. Figure 5‐6: 2011 MAG 2040 Project Map

shows no stations. Figure 5‐7: 2013 Project Info Sheet omits the loop north of UVU and its

stations. Figure 5‐8: 2014 EA Locally Preferred Alternative is a map that denotes which stations

are proposed as side stations and which are proposed as center stations. The station east of

BYU has been “split” into two stations: One northeast of BYU, closer to the Missionary Training

Center, and one further south at Provo 900 East. Figure 5‐9: December 2016 Project Map

shows no changes from the prior map.

5.4.2 GuidewayChangesOverTime

A significant change was the removal of the HOT interchange over I‐15. It appeared in Figure

5‐4: 2010 UTA Project Info Sheet but it was delayed until “Phase 2” in Figure 5‐5: 2011 EA

Locally Preferred Alternative and dropped thereafter. The location of exclusive lanes also

experienced substantial changes. In Figure 5‐6: 2011 MAG 2040 Project Map 900 East in Provo

had an exclusive lane, while there was none near the SouthGate Center. Figure 5‐8: 2014 EA

Locally Preferred Alternative also shifts part of the alignment from University Parkway to Orem

1200 South. Figure 5‐6: 2011 MAG 2040 Project Map omits the loop at the southern part. The

2013 Project info sheet (Figure 5‐7: 2013 Project Info Sheet) has only one change from the

2011 LPA: Orem 400 West from Orem 1200 South to University Parkway had an exclusive lane.

In Figure 5‐8: 2014 EA Locally Preferred Alternative, the alignment appears slightly altered. A

new road permits it to be shortened near the Orem FrontRunner station, and is “indented”

near BYU so that it turns north along Provo 700 East and then east at Provo 900 North, rather

than continuing on Provo 700 North to Provo 900 East. The 900 North segment is designated as

exclusive lanes. Provo 1860 East (near Novell) is also designated as having an exclusive lane.

Figure 5‐9: December 2016 Project Maps shows no changes. Over the course of project

development, the amount of exclusive guideway on University Avenue increased, while the

amount adjacent to BYU dropped by over half. Given that the BYU segment is in the middle of

the alignment, this reduction will likely have a negative impact on travel time reliability with a

corresponding effect on ridership.

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5.4.3 ChangesduetoCampusUnificationPlan

In 2013, BYU closed a portion of East Campus Drive as part of the Campus Unification Plan,

relocating a UTA station near the Wilkinson Student Center to a more remote location on the

edge of campus at about Provo 800 North & 900 East. This resulted in a relocation of the

planned BRT station, as well as changes in the location of the median guideway. The change

faced substantial neighborhood opposition. As a result, the portion of dedicated running way

along BYU was reduced.41 In February 2014, the Provo City Council voted against the LPA

(“Option 4”) in favor of an alignment following the 2007 MAG University Parkway/University

Avenue (“Option 0”).42 This decision was then provisionally reversed in March 2014, pending

additional information. A decision was made to hire an outside expert43 to perform an

independent analysis of the travel demand modeling of the alternatives.44 While the analysis

turned up some data errors in student and employment numbers as well as assumptions about

the subsidy BYU would provide, the final projections differed by about 10%, and the LPA was

recommended as the best option.

41 FAQ ‐ Provo‐ Orem Bus Rapid Transit Project. (n.d.). Retrieved May 17, 2017, from http://www.provo.org/Home/ShowDocument?id=3001 42 The Bus Rapid Transit Story. (May 4, 2015). Retrieved May 17, 2017, from

http://provomayor.com/2015/05/04/the‐bus‐rapid‐transit‐story/ 43 Provo City to Hire Third Party Researcher. (n.d.)Retrieved May 17, 2017, from http://provobuzz.com/provo‐city‐hire‐third‐party‐research‐brt/ 44 Rose, A. (2014, April 29). Independent report backs 900 East BRT route. Utah Valley 360. Retrieved May 17, 2017, from http://utahvalley360.com/2014/04/29/independent‐report‐backs‐900‐east‐brt‐route/

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6 Research Approach This section details the characteristic of the quasi‐experimental research design, the use of the

Provo‐Orem TRIP improvements as a “natural experiment,” and the use of an interrupted time

series for some variables. Null hypothesis testing is discussed and the conceptual framework is

explained. Finally, the data collection plan is detailed along with the geographic extent of the

analyses performed.

6.1 Quasi‐Experimental Research Design

In order to rigorously compare before and after conditions, this study uses a quasi‐

experimental design with non‐equivalent control groups. A quasi‐experimental research design

is an experimental research design that recognizes the impossibility of exactly replicating the

same experiment, but otherwise maintains all possible experimental controls. The control

group is considered non‐equivalent because of the lack of random assignment. Without

random assignment, quasi‐experiments rely on statistical control variables and sample

matching to show that alternative explanations are implausible. A causal inference from any

quasi‐experiment must meet the basic requirements for all causal relationships: that cause

precedes effect; that cause co‐varies with effect; and that alternative explanations for the

causal relationships are implausible.45

6.1.1 ANaturalExperiment

Experimental research design requires a pre‐test, a “treatment,” a post‐test, and a control

group. A natural experiment takes advantage of conditions where a treatment is applied to a

subset of a larger population. That subset is considered the treatment group, and the large

population becomes the control group. The control group is non‐equivalent because of the lack

of random assignment. In this case, the treatment consists of the improvements included in the

TRIP.

6.1.2 InterruptedTimeSeries

Performing quasi‐experimental analysis requires collecting data before and after the treatment.

Some variable data (e.g. traffic counts, transit ridership) for multiple years before the study

(2010—2015) were collected as part of the initial conditions report. Collecting this multiple‐

year time series makes it possible to project current trends into the future. Following the

45 Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi‐experimental designs for

generalized causal inference. Houghton, Mifflin and Company.

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construction period and the first year of operations, more data will be collected, extending the

time series. It will then be possible to compare the projected trends from the Initial Conditions

Report to actual values.

6.2 Null Hypothesis Testing

Confirmation bias is the tendency to favor information or evidence that supports a prior

hypothesis.46 Scientific research follows a series of rigorous protocols to avoid this, the most

common of which is the null hypothesis testing. The null hypothesis is an explicit declaration of

the expected relationship between cause and effect in the phenomena of interest. The formal

phrasing is that “there is no significant relationship between the two variables”. For this report,

the null hypothesis is that the project improvements will have no significant effect on any of the

outcome variables. Hypothetically, the level of development, parking occupancy, crash rates,

and total emissions should remain constant, while traffic growth and transit use should

continue to change in accordance with their previous trends.

6.3 Conceptual Framework

Conceptual framework is a term used to explicitly describe how different elements within a

system under study interact with one another. Figure 6‐1 shows the conceptual framework of

this study. Red (closed) arrows indicate a negative relationship between the elements; more of

one means less of the other, and vice versa. Black (open) arrows indicate a positive

relationship; more of one means more of the other. Green cylinders contain study variable

quantities. Blue ovals indicate unmeasured conceptual quantities. Green circles indicate ratios,

and yellow boxes indicate tables of rates.

Transportation demand on the alignment consists of three elements: background or through

travel, travel generated by other land uses along the corridor, and travel generated by the

universities. University‐induced travel demand is assumed to be a function of university faculty,

staff, and student enrollment. Total university parking occupancy is assumed to be a function of

parking supply and parking demand. Total parking supply is affected by University policies,

including the allocation for different classes of parking. Total parking demand is affected by gas

prices, the price of parking, and transit ridership. The location of parking lots in relation to

lecture buildings is expected to significantly influence location‐specific demand for parking. If

parking is not available near campus, other alternatives become more attractive.

46 Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of general

psychology, 2(2), 175.

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The amount of traffic congestion is a function of the difference between traffic on the

alignment (measured as AADT) and capacity on the alignment. The level of traffic congestion is

presumed to cause divergence to other times, modes, and routes. The dedicated guideway on

the BRT alignment is conceptualized to encourage a mode shift from automobile to transit, thus

reducing automobile traffic on the alignment, and thus lowering congestion on the BRT

alignment. AADT determines VMT, and thus vehicle emissions.

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Figure 6‐1: Conceptual Framework

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6.4 Data Collection Plan

Data collection about the initial conditions took place in 2015 and 2016. After construction

finishes, and a year of operations occurs, a second round of data collection will occur and a

second report will be released. A third round of data collection will occur the following year,

and a fourth round the year after that. Figure 6‐2 shows the current anticipated timeline for the

start of system operations.

Figure 6‐2: The November 2016 Project Timeline

6.5 Study Bounds – Geographic Extents

Different analyses make use of different geographic extents. Traffic counts, VMT, and vehicle

emissions were analyzed for the alignment and the identified diversion corridors. Crash rates

were analyzed for the alignment itself. For land development and trip generation, a ¼‐mile

buffer around the alignment was used based on 2010 Decennial Census Tracts/MAG 2010

Traffic Analysis Zones. For parking supply, all dedicated parking for the two universities was

considered. The following subsections contain additional information about the diversion

corridors, corridor changes, and the buffered alignment.

6.5.1 DiversionCorridors

On facilities near capacity, or at capacity during peak times, a phenomenon known as diversion

occurs. This is where drivers choose to drive a longer amount of time along indirect routes on

less congested roads. Diversion represents a “latent demand” for travel capacity on one street

that is being met by capacity on parallel streets. It has been speculated that BRT operations

may impair traffic flow, thus increasing congestion and causing drivers to divert to other roads.

There is already substantial diversion from the most direct routes, according to local planners.

For example, while the University Parkway exit is the most direct route to University Mall and

BYU, using it still requires miles of travel along surface streets to reach BYU. Consequently,

travelers are already diverting to alternate routes miles away, making use of all alternative

roads leading eastbound from I‐15 interchanges.

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To determine if the BRT is causing additional diversion, it is necessary to measure the traffic

volumes on potential diversion corridors. The possibility of diversion was investigated by

quantifying changes in traffic volumes on alternate routes near the BRT corridor, including I‐15.

These routes were identified in consultation with local planners in Provo and Orem.

In addition to diversion corridors, screen lines and cordon lines were used to capture changes in

automobile volumes. A screen line cuts across the whole of an area and sums the total volume

of all roads along that line. A cordon line is like a screen line, except it wraps around all sides of

a district. In this study, BYU has a cordon line. The diversion corridors, screen lines, and BYU

cordon are shown in Figure 6‐3.

The east‐west diversion corridors are:

Orem 800 North,

Orem Center Street,

Orem 800 South,

Orem 1200 South,

University Parkway,

Provo 2230 North,

Bulldog Boulevard/Provo 1230 North,

Provo 820/800 North,

Provo Center Street, and

Provo 300 South.

The north‐south diversion corridors are:

Geneva Road,

I‐15,

State Street/Highway 89,

Orem 800 East,

Provo 500 West,

Provo 200 West/Freedom Boulevard,

University Avenue/Highway 189, and

Provo 900 East.

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Orem 800 South was included due to the planned interchange.47 Highway 189 was also

included as a diversion corridor to guard against the possibility of traffic diverting from

University Avenue. Provo Center Street has also been included, as it provides another

alternative path. However, substantial diversion to Provo Center Street is unlikely, due to the

traffic calming techniques (on‐street parking, narrow lanes, and lowered speeds) applied in the

Provo CBD.

47 Wasatch Front Regional Council. (2013). Utah’s Unified Transportation Plan 2011‐2040. Retrieved from: http://www.wfrc.org/new_wfrc/UnifiedPlan/Unified%20Plan%20Booklet%20Web%20Version%20Final%206%20Aug.%202013.pdf

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Figure 6‐3: Map of Diversion Corridors and Screen Lines

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6.5.2 FutureAlignment

There is a possibility that the alignment of the BRT may change during the evaluation period. A

new high occupancy vehicle (HOV) or high occupancy toll (HOT) interchange at I‐15 and Orem

820 North may be constructed.48 This is shown in Figure 6‐4.

Figure 6‐4: Map of HOV Exchange at 800 South in Orem

48 Utah Transit Authority, & Mountainland Association of Governments. (2010, May 31). Provo‐Orem Rapid Transit Environmental Assessment: Project at a Glance. Retrieved May 17, 2017, from https://www.mountainland.org/img/minutes/Regional_Planning/2010/2010_09_02/BRT%20Project%20Info%20Sheet.pdf

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The HOV/HOT interchange were under study in April 2014.49 They were included in the 2011

EA, and they show up on MAG’s TranPlan40 map50 as “Unfunded” in Phase 1 (2015—2024), but

only corridor preservation is included in the 2017‐2022 Transportation Improvement Program

for the MAG 2040 plan.51 A new HOV/HOT interchange would permit vehicles to access UVU

more directly from I‐15, removing some UVU‐bound traffic from University Boulevard. It would

also improve the operations of the Provo‐Orem BRT by avoiding several congested

intersections, thus improving travel time and route attractiveness.

6.5.3 BufferedAlignment

For land development and trip generation, a ¼‐mile buffer around the alignment was used

based on 2010 Decennial Census Block Groups. Census geography was used for the buffer to

provide a control total for changes in population and employment. Block Groups were the

finest scale of geography for which the five‐year American Community Survey Data was

available.52 The Block Groups selected include those adjacent to both the initial year of

alignment and final year of alignment. In most cases, the 2010 block group polygons are the

same as MAG traffic analysis zone geometry. The buffered alignment used is shown in Figure

6‐5.

49 Mountainland Association of Governments. (n.d.). Transportation studies Recommended. Retrieved May 17, 2017, from https://www.mountainland.org/img/minutes/Regional_Planning/2014/2014_04_03/Transportation%20Studies%20Recomended.pdf 50 Mountainland Association of Governments. (2016, August). TransPlan40. Retrieved from http://mag‐gis.maps.arcgis.com/apps/MapSeries/index.html?appid=b7bc635f4a6c445886b29e0ce25a19ac 51 Mountainland Association of Governments. (2016, August 4). Mountainland MPO TIP 2017‐22 Program. Retrieved May 17, 2017, from http://mountainland.org/img/transportation/TIP/2017%20TIP/2017%20TIP%20Final.pdf 52 US Census Bureau. (2016, October 17). Selected Geographic Areas Published in the 2015 American Community Survey 1‐year and 5‐year estimates. Retrieved May 17, 2017, from https://www.census.gov/programs‐surveys/acs/geography‐acs/areas‐published.html

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Figure 6‐5: Map of Study Area Development Boundaries

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Using a buffer to select polygons automatically was problematic as it consistently resulted in

inappropriate selections. Using the “select by intersect” option allowed for selected polygons

with only a small portion adjacent to the alignment, while using “select by centroid” excluded

large polygons adjacent to the alignment. The final selection was created by using a ¼‐mile

buffer to select 2010 census block group polygons, and then excluded a limited number of

insufficiently related polygons. Blocks excluded as insufficiently related were those with the

following GEOIDs: 490490014012, 490490022011, 490490028021, and 490490022073. This

selection was used to select parcels for the land development and trip generation analyses.

Controlling for the effects of new development in other locations (such as the diversion

corridors) would require tracking land use changes for a much larger area. Controlling for the

complex interactions between trip generation and roadway volumes over a larger area is a task

more suitable for a travel demand model. The data acquisition and processing costs of doing so

were also deemed prohibitive. Within the buffered alignment alone, there were about 14,600

parcels that were classified by land use.

6.6 Data Source Limitations

The analyses in this report include data for the five years prior to the beginning of construction.

Utility work for construction on the Provo‐Orem BRT began in late 2015 and early 2016. In

some cases, data for all of 2015 was not yet available, therefore data were collected for as long

as was available. In other cases, due to limited data availability, data were collected for 2010—

2014. Specifics on data source limitations are included in their respective sections in the

Findings section. This report presents the data collected to date since evaluation activities

commenced on May 30, 2015.

6.7 Timing of Traffic and Ridership Changes

Previous research suggests that drivers rapidly adapt to new roadway capacity, altering routes

and travel patterns in a matter of weeks.53 Transit riders tend to be less adaptable. Many riders

are familiar with only a single route rather than the entire transit system. Consequently, the

short‐run elasticities of change in ridership in response to service are much less than the long‐

run elasticities54 ‐ it takes longer for riders to discover a new service. The amount and quality of

public information provided is critical to facilitating service uptake for choice riders. Travelers

53 Dowling, R. G., & Colman, S. B. (1995). Effects of increased highway capacity: results of household travel behavior survey. Transportation Research Record, 1493, 143-149. 54 Litman, T. (2004). Transit price elasticities and cross-elasticities. Journal of Public Transportation, 7(2), 3.

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tend to switch between modes when familiar commuting patterns are disturbed, such as when

changing place of work or place of residence55. As the Provo‐Orem BRT will serve two

universities, there will be substantial turnover in the population of travelers. Each year there

will be a fresh batch of students trying out different travel arrangements.

55 Frank, L., Bradley, M., Kavage, S., Chapman, J., & Lawton, T. K. (2008). Urban form, travel time, and cost relationships with tour complexity and mode choice. Transportation, 35(1), 37-54.

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7 Evaluation of System Performance This section provides an evaluation of the system performance as measured by travel time,

reliability, image and identity, safety and security, and capacity.

7.1 Travel Times

Scheduled travel time from the Orem Intermodal Center to the Provo Intermodal Center on

Route 830 is 37 minutes, although Route 830 does not cover the whole alignment. Travel time

for the No‐Action/Enhanced Bus alternative in the 2011 EA was 45 minutes,56 and 34 minutes

for the Bus Rapid Transit. End‐to‐end travel time was forecast to be 38 minutes for the 2014 EA

alignment.57

7.2 Schedule Reliability

Operating in mixed traffic, Route 830 is currently subject to traffic congestion and thus to

congestion‐induced delays that any driver suffers from. The need to make regular stops to pick

up and drop off passengers slows bus travel further. Without special boarding stations, busses

typically must leave a traffic lane to pick up passengers and then suffer delays merging back

into traffic. All of these delays are worse during the peak hour as congestion is worse, the

number of passengers is larger, and higher traffic makes merging more difficult. The BRT can be

expected to enjoy substantial schedule reliability over a normal bus route. UTA forecasts its

reliability to be greater than 95 percent.58

7.3 Identity & Image

Utah currently operates only non‐articulated buses. The Provo‐Orem BRT will operate modern

articulated buses59 that will make them immediately distinguishable from regular buses. UTA’s

other Bus Rapid Transit Route is branded as “MAX” and has a special route designation (35M),

but the agency otherwise uses standard buses. The S‐line Streetcar is branded as the “Silver

56 Utah Transit Authority. (2011, April 12). Provo‐Orem Bus Rapid Transit Environmental Assessment‐ Chapter 5: Comparisons of Alternatives. Retrieved May 20, 2017, from http://www.rideuta.com/uploads/10_Provo‐Orem_BRT_EA_April2011_Ch5_Alts_Comparison.pdf 57 Rahlf, R. (2015, May 31). Provo‐Orem Bus Rapid Transit system is the right investment. Deseret News. Retrieved May 20, 2017, from http://www.deseretnews.com/article/865629773/Provo‐Orem‐Bus‐Rapid‐Transit‐system‐is‐the‐right‐investment.html?pg=all 58 Utah Transit Authority. (2011, April 12). Provo‐Orem Bus Rapid Transit Environmental Assessment‐ Chapter 5: Comparisons of Alternatives. Retrieved May 20, 2017, from http://www.rideuta.com/uploads/10_Provo‐Orem_BRT_EA_April2011_Ch5_Alts_Comparison.pdf 59 Utah Transit Authority. (n.d.). Provo‐ Orem Transportation Improvement Project. Retrieved May 20, 2017, from https://i4.rideuta.com/mc/?page=Projects‐Provo‐Orem‐Transportation‐Improvement‐Project

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Line” and while it uses standard light rail vehicles, they have a special livery and are never

coupled into multiple car trainsets. Intended branding and livery for the Provo‐Orem BRT have

not yet been announced, and no vehicles have yet been purchased.

7.4 Safety & Security

This subsection covers the safety and security of bus rapid transit systems.

7.4.1 Safety

Riders on mass transit vehicles are typically safer in a crash than most automobile passengers.

The combination of professionally trained drivers and the greater mass of the vehicles means

that collisions are both less frequent and less impactful. However, rapid transit systems without

fully grade‐separated guideways are less safe than systems with grade‐separated guideways

(such as underground and elevated metro systems). Rapid systems without grade separation

make do with much cheaper “time separation” via stop‐control provided by railroad gates and

traffic lights.

Some of the most severe crashes for both BRT and light rail tend to occur at railroad gates,

either when the gate malfunctions or a driver attempts to bypass the gate. This typically results

in a high‐speed collision between the automobile and the much larger transit vehicle. Fatalities

often result. Such crashes tend to occur early on in operations, before drivers learn to expect

the transit vehicles.

Stop‐controlled intersections where automobile traffic is perpendicular to the transit vehicle

constitute the second most dangerous conflict point. Drivers running red lights or making illegal

turns are a common cause, especially on higher speed streets. High speeds result in limited

peripheral vision and reduce time available to react. TSP measures may worsen this effect;

triggered priority may rearrange the signal phases of a traffic signal so that the signal acts

contrary to driver expectations of phase length or signal sequence. Again, this problem is most

prevalent when operations first begin. To avoid this issue, drivers should be directed to proceed

slowly when entering a newly “green” intersection.

The next most likely location of conflicts between transit vehicles occurs where a vehicle

leaving an exclusive guideway merges with shared traffic. For the Provo‐Orem BRT, almost all

transitions from exclusive to shared guideways will take place at intersections, which should

help mitigate this issue. However, shared turn‐pockets are a common conflict location (the left

turn lanes for center‐running BRT and/or and right turn pockets for side‐running BRT).

For more detail about safety and Bus Rapid Transit, consult APPENDIX A. LITERATURE REVIEW.

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7.4.2 Security

The reduced number of stops along the BRT should improve the perception of safety by

reducing likelihood of riders waiting alone at a stop.60 Most train cars, platforms, and buses

have installed security cameras. Which stations of the Provo‐Orem BRT will have cameras has

not been decided or announced. UTA has its own police force, the Utah Transit Authority Public

Safety Department, and the agency has been commended for its level of commitment to

security.61

7.5 Capacity

The recently purchased New Flyer Xcelsior buses may or may not be intended for the BRT.62

Assuming an articulated bus is purchased, passenger capacity per bus can be estimated at 90

passengers for an articulated New Flyer vehicle,63 or 108 for an Xcelsior vehicle. If the IRIS Civic

Bus, used for the Las Vegas MAX, were purchased, each bus would have a capacity of 120

persons. With five‐minute peak headways, this equates to 12 buses per hour per direction.

Figure 7‐1 shows the maximum capacity such vehicles could provide. Also included are the 2011

EA estimates of peak hour utilization.64

Figure 7‐1: Peak Hour Transit Capacity

60 Schimek, P., Darido, G., & Schneck, D. (2005, September). Boston Silver Line Washington Street BRT Demonstration Project Evaluation. Retrieved May 20, 2017, from https://nbrti.org/media/evaluations/Boston_Silver_Line_final_report.pdf 61 Transportation Security Administration. (2013). TSA commends 16 mass transit and rail agencies for highest security levels [Press release]. Retrieved May 20, 2017, from https://www.tsa.gov/news/releases/2013/07/10/tsa‐commends‐16‐mass‐transit‐and‐rail‐agencies‐highest‐security‐levels 62 Utah Transit to add 35 more 60‐foot New Flyer Xcelsiors. (2016, December 15). Metro Magazine. Retrieved May 20, 2017, from http://www.metro‐magazine.com/bus/news/719169/utah‐transit‐to‐add‐35‐more‐60‐foot‐new‐flyer‐xcelsiors 63 Thole, C., Cain, A., & Flynn, J. (2009, April). The EmX Franklin Corridor BRT Project Evaluation. Retrieved May 20, 2017, from https://www.nbrti.org/docs/pdf/EmX_%20Evaluation_09_508.pdf 64 Utah Transit Authority. (2011, April 12). Provo‐Orem Bus Rapid Transit Environmental Assessment‐ Chapter 5: Comparisons of Alternatives. Retrieved May 20, 2017, from http://www.rideuta.com/uploads/10_Provo‐Orem_BRT_EA_April2011_Ch5_Alts_Comparison.pdf

…per direction Total

New Flyer 90 12 1080 2160

New Flyer Xcelsior 108 12 1296 2592

Iris Civic Bus 120 12 1440 2880

UTA Forecast Bus ? 12 164‐330 328‐660

UTA Forecast BRT ? 12 720‐960 1440‐1920

Passengers per hourBuses per

Hour, per CapacityVehicle

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Together, this suggests that an articulated New Flyer vehicle might be able to handle peak

loads, but with a very low margin for error (about 10 persons per bus), while an New Flyer

Xcelsior will have a margin of error of 28 riders and be able to handle the heaviest of peak

loads.

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8 Assessment of System Benefits This section contains analyses of transit ridership, capital cost effectiveness, operating cost

efficiency, transit supportive land use development, and environmental quality.

8.1 Transit Ridership

Total transit ridership is affected by a large number of factors. Mode choice decisions are made

on the basis of time and the financial costs of different modes. Given that transit vehicles must

make stops to pick up and drop off passengers, transit is often much slower than a personal

automobile. However, it is possible to bypass congestion if transit vehicles can make use of a

dedicated guideway.65 Better service frequency reduces the average wait time for a transit

vehicle, bringing it closer to parity with the private automobile. Service frequency determines

the average wait time for transit, and thus much of the overall travel time for a trip. Thus, the

service levels can significantly affect ridership. As service provision fluctuates so does ridership.

Figure 8‐166 shows the location and frequency of bus routes near UVU in August 2015.67 Green

indicates 15‐minute service, Yellow 30‐minute service, Blue 60‐minute service, and Red peak‐

hour service. Figure 8‐268 shows transit routes for the study area in 2015, Figure 8‐3 shows the

routes for 2016, and Figure 8‐4 shows transit routes around BYU.

65 Dittmar, H., & Poticha, S. (2004). Defining Transit Oriented Development. In Presentation for TOD Seminar, School of Architecture, The University of Texas at Austin. Spring. 66 Utah Transit Authority. (n.d.). Utah County Systems Map‐April 2016. Retrieved May 20, 2017, from https://www.rideuta.com/‐/media/Files/System‐Maps/2016/Utah‐County‐System‐Map.ashx 67 Utah Transit Authority. (n.d.). Utah County Systems Map‐April 2015. Retrieved May 20, 2017, from https://www.rideuta.com/‐/media/RideUTA/Maps/UtahcountymapApril2015.ashx 68 Utah Transit Authority. (n.d.). Utah County Systems Map‐April 2016. Retrieved May 20, 2017, from

https://www.rideuta.com/‐/media/Files/System‐Maps/2016/Utah‐County‐System‐Map.ashx

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Figure 8‐1: Transit Routes near UVU

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Figure 8‐2: Transit Routes and Headway in Study Area 2015

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Figure 8‐3: Transit Routes in Study Area 201669

69 Utah Transit Authority. (n.d.). Utah County Systems Map‐April 2016. Retrieved May 20, 2017, from https://www.rideuta.com/‐/media/Files/System‐Maps/2016/Utah‐County‐System‐Map.ashx

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Figure 8‐4: Transit Routes near BYU 201570

8.1.1 Methods&Data

Data for all transit routes within the study area were collected, including FrontRunner. All data

are UTA counts. For bus ridership, data prior to 2014 are driver counts and are only available at

the route level. Passenger counts for 2014—2015 are Automatic Passenger Counter (APC) data.

Driver counts are available only at the route level, while APC counts are available at specific

locations. Line‐level data for 2014—2015 are sums of boardings/alightings. FrontRunner began

operations in Utah County in 2012. For FrontRunner, passenger counts for 2012—2015 were

available at the station level. (Passenger counts for FrontRunner prior to December 2012 are

only available at the system level.)

70 Utah Transit Authority. (n.d.). Utah County Systems Map‐April 2016. Retrieved May 20, 2017, from https://www.rideuta.com/‐/media/Files/System‐Maps/2016/Utah‐County‐System‐Map.ashx

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8.1.2 Results&Discussion

Ridership analyses in this section include system level ridership for Utah County, analysis of

FrontRunner ridership for the Provo and Orem Stations, analysis of total FrontRunner ridership,

and analysis of Utah County bus ridership.

8.1.2.1 SystemwideRidership

Figure 8‐5 shows data for transit ridership in Utah County for both bus and FrontRunner. (Prior

to the advent of FrontRunner, total ridership and bus ridership were identical). The total trend

line is the trend line for the system from 2008‐2015. Data are monthly totals, excepting UTA’s

thrice‐yearly ‘change day’ counts, when there are two counts in a single month.

Figure 8‐5: Transit Ridership in Utah County

The trend line shows that bus ridership was in decline 2008‐2012, but trends changed in 2013

when FrontRunner South began operations. Post initiation of FrontRunner South, Utah County

bus ridership rose. FrontRunner South ridership over time for the two stations within the study

area (Orem Central and Provo Central) are shown in Figure 8‐6.

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Figure 8‐6: FrontRunner Ridership 2009—2015 for Orem Central and Provo Central Stations

Six years of data for bus ridership in Utah County is shown in Figure 8‐7. The monthly ridership

for each year is presented as a distinct line.

Figure 8‐7: Utah County Bus Ridership by Month 2010‐2015

Total ridership is about the same for 2010 and 2011, while 2012 ridership is lower and 2013

ridership the lowest of all. There was an increase from May onward for 2014, while 2015 bus

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ridership is nearly identical to 2014. Changes in ridership pre‐ and post‐FrontRunner South can

be clearly seen. After the opening of FrontRunner South in 2012, UTA heavily reorganized the

bus system, cutting half a dozen routes and combining others. Many of the routes that were cut

were inter‐county express routes. The graph also clearly demonstrates the effect that school

enrollment (such as at major universities) has on transit usage. Ridership drops substantially

between May and August while schools are out of session.

For the BRT alignment, Route 830 is most comparable. Figure 8‐8 shows the annual ridership71

of Route 830. It shows two different data series, as UTA switched its data collection method

from driver counts to Automatic Passenger Counters.

Figure 8‐8: Annual and Daily Ridership for the BRT

Route 830 ridership rose from 2000 to 2004, and has declined since. In 2011, when forecasts

were made, it had remained steady at about 50,000 annual riders for several years. Post 2011,

ridership declined until 2014 when it stabilized at a new low. Much of this change can be

attributed to changes in service and routing.

8.1.2.2 ChangesinTransitSupplyandRoutingoverTime

UTA makes changes to routes and route frequency 3 times a year, an event known as “Change

Day,” and UTA uses a spreadsheet to track these changes. Change Day Inventory shows that at

the end of 2011, extra trips to BYU were discontinued, which seems to be the cause of the drop

in ridership between 2011 and 2012. Then, at the end of 2012, route 830 was extended to serve

71 For driver counts, these represent riders. For automatic passenger counters, these represent boardings.

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the Orem and Provo FrontRunner stations. In 2013, route 830 was then realigned to serve a

new bus stop, and then changed to be identical to the BRT alignment. In April 2014, it was re‐

routed to use Geneva Road, rather than passing through Wolverine Crossing (UVU student

housing), which is about 550 feet away. All of these changes appear to have resulted in reduced

ridership. Figure 8‐9 features a map of the April 2011 Route 830.

Figure 8‐9: Route 830, April 2011

As can be seen, the route was considerably different. It included detours to reach the

Timpanogas Transit Center and Wyview Park. It passed by Carriage Cove Apartments, the Utah

Valley Hospital, Provo High School, and ran along Campus Drive through BYU.

8.1.2.3 ComparingCountsandForecasts

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Average daily ridership for Route 830 is shown in Figure 8‐10. The “Weekday” line represents

daily ridership on an average weekday, while the “Daily” line represents an average ridership

across all days of the week. The data are based on driver counts, which occur once or twice a

month.

Figure 8‐10: Estimated Average Daily Ridership, Driver Counts, Route 830

The average weekday count is uniformly higher than the average daily count, reflecting higher

usage on workdays than on weekends. The ratio between the two is roughly constant.

Daily Ridership for the APCs is shown in Figure 8‐11. Unlike driver counts, the automatic counts

are performed continuously. This makes is possible to provide monthly totals, and to provide

totals both before and after UTA’s thrice‐annual Change Day.

Figure 8‐11: Estimated Average Daily Ridership, APC, Route 830

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The APC counts show a wider range of monthly variation in transit ridership than the driver

counts. However, the average ridership for APC counts (2,470 for 2014 and 2,404 for 2015) are

very similar to the driver count for 2014 of 2,457.

These counts are substantially different from the forecast daily ridership for an enhanced bus

and the BRT from the 2011 EA.72, show in Figure 8‐12.

Figure 8‐12: Forecast Route Ridership, in riders/day

The forecasted 2014 ridership of 6,800 appears high when compared to the 2014 estimates of

actual ridership. This can be explained by a number of factors: First, the timing of projections.

The forecast was made in 2011, which meant 2010 data would have been the most recent

available. Second, in 2010 ridership was at what proved to be a 5‐year peak, of about 3,400

riders. Third, the alignment used for these forecasts is not identical to route 830; it runs the

entire BRT alignment, from Orem FrontRunner to the technology park, and includes what is

currently route 838. Including the ridership on route 838, which represents a total daily

ridership of about 3,500 riders. The final (and likely more significant) factor, concerns changes

University support for transit.

8.1.2.4 ChangesinUniversitySupportforTransit

From 200273 to 2010, BYU participated in the UTA Eco Pass and Ed Pass programs. In 2003—

2004, the passes were free to students and $60 annually for employees.74 In 2008—2009, the

pass was available to students for $120 a semester when a regular monthly pass was $160.75 In

2010, changes in the UTA Ed Pass program resulted in an increase in the cost per pass provided,

and BYU ceased to subsidize the passes. BYU continued to offer passes at a 25 percent discount

72 Utah Transit Authority. (2011, April 12). Provo‐Orem Bus Rapid Transit Environmental Assessment‐ Chapter 5. Retrieved from http://www.rideuta.com/uploads/10_Provo‐Orem_BRT_EA_April2011_Ch5_Alts_Comparison.pdf 73 Warnock, C. (2002, March 22). BYU to join UTA's Eco Pass program. The Daily Herald. Retrieved May 20, 2017, from http://www.heraldextra.com/news/byu‐to‐join‐uta‐s‐eco‐pass‐program/article_67be06a8‐170f‐58c4‐85d6‐3d5fab8943cd.html 74 Jenkins, C. P. (2003, April 13). Student UTA stickers available April 18. BYU News. Retrieved May 20, 2017, from https://news.byu.edu/news/student‐uta‐stickers‐available‐april‐18 75 Jenkins, C. (2008, June 22). BYU continues to offer UTA Ed Pass at a bargain price. BYU News. Retrieved May 20, 2017, from https://news.byu.edu/news/byu‐continues‐offer‐uta‐ed‐pass‐bargain‐price

Year/Alternative 2014 2030

Enhanced Bus 6,800 8,400

Preferred Alternative BRT 12,900 16,100

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UTA then offered universities.76 In 2011, a regular monthly pass was $134, so a student pass

would have been $100. Accordingly, the cost of the pass to users increased from $240 a year to

an estimated $1,200 a year ($100 a month). In late 2011, UTA began to offer semester‐long

passes for $212, of which BYU offered a limited number of $50 subsidies,77 with a total limit of

$150,000 (about 3,000 passes). This program was continued in 201278 and continued in 2015.

At UVU, passes cost $20 in 2011 as part of the Ed Pass program.79 In 2012, passes were $50,

and have been rising $10 each year.80 In fall 2015, UVU sold UTA transit passes (valid on all

services except Ski Bus and Para‐transit) for $90 per year.81 In 2015, the rate for an adult pass

was $198, and the price for a K‐12 student was $150, representing a substantial subsidy. The

same system was continued in 2017.82 Hence, while UVU continues to provide a pass, it

continues to increase the price to students.

8.1.2.5 TheRyde

A fourth factor affecting ridership is The Ryde. Beginning in 2012, this private shuttle service

began to provide a series of short‐distance shuttle routes in a limited area near BYU.83 It runs

three times an hour84 and students are able to use their student IDs as pass cards.85 There are

five routes,86 which are shown in Figure 8‐13 and Figure 8‐14. Two routes terminate at the

76 Leonard, W. (2011, May 31). UTA in Ed Pass negotiations with colleges, universities. Deseret News. Retrieved May 20, 2017, from http://www.deseretnews.com/article/705373652/UTA‐in‐Ed‐Pass‐negotiations‐with‐colleges‐universities.html?pg=all 77 Call, A. (2011, September 5). UTA’s Newest Student Transit Pass. The Daily Universe. Retrieved May 20, 2017, from http://universe.byu.edu/2011/09/05/utas‐newest‐student‐transit‐pass/ 78 Fielding, C. (2012, August 14). BYU employees can purchase UTA passes for upcoming school year beginning Aug. 20. BYU News. Retrieved May 20, 2017, from https://news.byu.edu/news/byu‐employees‐can‐purchase‐uta‐passes‐upcoming‐school‐year‐beginning‐aug‐20 79 Rosenlof, C. (2011, February 28). Thrown Under the Bus. The UVU Review. Retrieved May 20, 2017, from http://www.uvureview.com/recent/opinions/thrown‐under‐the‐bus/ 80 Frandsen, T. (2014, October 6). UTA Raises Price for Student Pass. The UVU Review. Retrieved May 20, 2017, from http://www.uvureview.com/recent/news/uta‐raises‐price‐student‐pass/ 81 Frandsen, T. (2014, October 6). UTA Raises Price for Student Pass. The UVU Review. Retrieved May 20, 2017, from http://www.uvureview.com/recent/news/uta‐raises‐price‐student‐pass/ 82 Utah Valley University Campus Connection. (n.d.). Retrieved May 20, 2017, from https://www.uvu.edu/campusconnection/id/index.html 83 Jenkins, C. (2014, December 4). BYU Announces Student Shuttle Service and Changes to Student Parking. BYU News. Retrieved May 20, 2017, from https://news.byu.edu/news/byu‐announces‐student‐shuttle‐service‐and‐changes‐student‐parking 84 BYU OneStop. (n.d.). Retrieved May 20, 2017, from https://onestop.byu.edu/view‐transportation‐options 85 The Ryde. (n.d.). Retrieved May 20, 2017, from http://studentmovement.com/ 86 The Ryde. (n.d.). Retrieved May 20, 2017, from The Ryde. (n.d.). Retrieved May 20, 2017, from http://studentmovement.com/

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roundabout at East Campus Drive and 1100 North. Three routes terminate at the BYU Museum

of Art Parking lot.

Figure 8‐13: The RYDE routes 2016

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Figure 8‐14: The RYDE routes 2015

A model made using assumptions of 2010 conditions (2010 ridership, 2005—2010 trend, 2010

travel times, 2010 university support, the absence of The Ryde) would over‐predict 2011

ridership, being unable to incorporate information about changed conditions in 2011.

8.1.2.6 BoardingLocations

Boardings and alightings on the current Route 830 and Route 838 are shown in Figure 8‐15. The

largest numbers for Route 830 take place at Orem FrontRunner, UVU, the Timpanogas transit

center, the southeast corner of BYU, and Provo FrontRunner. For Route 838, the largest

number of boardings take place in the East Bay Technology Park.

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8.1.3 DataLimitations

From 2000 to 2014, UTA made use of two counts: a monthly driver count and an FTA‐approved

random sampling plan by system monitors. The two counts initially differed significantly.

During this audit, we found that UTA reached adequate sampling levels in 2010 (as

prescribed by the Federal Transit Administration (FTA)) and that the difference between

the system monitor and bus driver counts has decreased from large variations of 30

percent in 2004 and 12 percent in 2007. It should be noted that a smaller percent of

variation indicates more reliable data.87

Part of the divergence was found to be a result of system monitors including themselves in the

passenger total. The amount of variance also fell over time. "After adjusting for the system

monitor count overstatement of 6 percent, it appears that the variance between bus driver

counts and system monitor counts was actually 8 percent in 2010.88

In 2014, UTA equipped all buses with automatic passenger counters. An evaluation of ridership

counts in 2014 found the APC counts to be very similar to the adjusted driver counts (about

2.7%89). Hence, while older counts can be questionable, recent counts should be accurate and

unbiased. In 2015, the FTA approved the use of the automatic passenger counters for sampling,

and UTA discontinued the use of driver counts and system monitor counts. Hence, there are

two different data series in ridership data.

The latter data is of better quality. For UTA, ridership data prior to 2014 was only recorded at

the route level, so the locations of boardings and alightings can only be known for 2014 and

2015.

No data on ridership for The Ryde is currently available, so it is unknown how the addition of a

private (but subsidized) transit service has affected total transit ridership.

87 Office of the Utah Legislative Auditor General. (2012, January). A Performance Audit of the Utah Transit Authority. Retrieved May 20, 2017, from https://le.utah.gov/audit/12_01rpt.pdf 88 Office of the Utah Legislative Auditor General. (2012, January). A Performance Audit Oif the Utah Transit Authority. Retrieved May 20, 2017, from https://le.utah.gov/audit/12_01rpt.pdf 89 Utah Transit Authority. (n.d.). Comprehensive Annual Financial Report‐ For Fiscal Year Ended December 31, 2014 and 2013. Retrieved May 20, 2017, from https://www.rideuta.com/uploads/UTA2014CAFRReport.pdf

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Figure 8‐15: Boardings and Alightings for Routes 830 and 838 in 2015

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8.2 Capital Cost Effectiveness

FTA guidelines for cost effectiveness vary by project type90:

The cost effectiveness measure for New Starts projects is the annual capital and

operating and maintenance (O&M) cost per trip on the project. For Small Starts projects,

the cost effectiveness measure is the annualized capital federal share of the project per

trip on the project. The number of trips on the project is not an incremental measure but

simply total estimated trips on the project.

The Provo‐Orem BRT project was funded under a Small Starts grant, so the appropriate

measure is annualized federal share of the project per trip on the project. Totals from a 2011

forecast provided counts in terms of daily linked trips on the project91. The annualized federal

share was calculated on a $75 million federal grant. The capital share was annualized to obtain

an equivalent annual cost by dividing the net present value of the present value of an annuity.

In accordance with FTA guidance, a 2.0 percent discount rate was used, with a 20 year horizon.

Figure 8‐16 shows the calculation used92.

Figure 8‐16: Equivalent Annual Cost Calculation

This calculation provides a value of $4,586,754 for the annualized cost. The resultant capital

cost effectiveness is shown in Figure 8‐17.

90 Federal Transit Administration (2013) “New Starts and Small Starts Evaluation and Rating Process Final Policy Guidance 2013”. Retrieved from: http://www.apta.com/gap/fedreg/Documents/NS‐SS_Final_PolicyGuidance_August_2013.pdf 91 Federal Transit Admininistration, Region VIII. (2011). Environmental Assessment, Provo‐Orem Rapid Transit Project, Utah County, Utah. Salt Lake City, Utah: Utah Transit Authority. 92 Federal Transit Administration (2013) “New Starts and Small Starts Evaluation and Rating Process Final Policy Guidance 2013”. Retrieved from: http://www.apta.com/gap/fedreg/Documents/NS‐SS_Final_PolicyGuidance_August_2013.pdf

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Figure 8‐17: Capital Cost Efficiency based on 2011 Forecast

8.3 Operating Cost Efficiency

Efficiency is defined as the unit cost of inputs necessary to produce a unit of output. From a

transit perspective, this is typically the operating cost per passenger. While the system has not

yet begun operations, this can be estimated for the project by dividing the annual operating

costs by the annual linked trips to get the cost per trip. The annual operating cost was forecast

to be $3.59 million93. This generates a cost per trip of $1.02.

Other operating cost metrics include cost effectiveness, such as cost‐per‐vehicle‐hour or vehicle

mile, farebox recovery ratio, or net subsidy per passenger are not presented here. Measures of

service productivity such as passengers per vehicle mile or vehicle hour, while valuable, are not

relevant to operating cost efficiency.

8.4 Transit Supportive Land Use Development

This section contains information on current land uses, potential future land uses, and the

timing of land uses changes.

8.4.1 CurrentLandUses

The major land use near the project alignment is educational. Educational land uses include

UVU, BYU, and Provo High School. Commercial land uses make up the next largest category,

followed by residential uses.

93 Federal Transit Administration. (2017) Provo‐Orem Bus Rapid Transit: Provo‐Orem, Utah Small Starts Development. Retrieved from: https://www.transit.dot.gov/sites/fta.dot.gov/files/docs/UT__Provo‐Orem_BRT_Profile_FY17.pdf

Metric Value Cost Effectiveness

Daily Linked Trips 11,300 405.91$

Annual Linked Trips 3,533,200 1.30$

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Figure 8‐18 shows the study area land uses.

8.4.2 PotentialFutureLandUse

Almost all the land within the study area has already been urbanized, so almost all new

development must be redevelopment of existing urbanized land. For most urban land,

redevelopment tends to occur only when the value of existing structures is negligible. A

substantial portion of the area near the project alignment consists of single‐family homes,

which rarely depreciates to such a level, and thus are extremely unlikely to redevelop into

transit‐supportive land uses. However, much of the land consists of automobile‐oriented

commercial use, which tends to depreciate rapidly.94 Accordingly, it seems likely that these

parcels will redevelop most quickly. Depending on the real estate and regulatory context, this

may result in subdivision into smaller parcels, the construction of additional structures closer to

the roadway, and may include the addition of parking garages. It may also result in a transition

to multi‐family apartment houses. Low‐rise commercial use tends to generate a very large

number of trips of very short duration. Apartments, especially those with limited parking, tend

to generate more transit trips.

For non‐market actors such as universities, the allocation of real estate follows different

principles. In general, it tends to transition from lower intensity uses to higher intensity uses

over time, typically from lawns and other open space to parking lots and then to buildings and

parking garages.

8.4.3 TimingofLandUseChanges

The timing of land use changes in response to transit improvements is highly variable. Research

suggests that the timing of redevelopment depends on four factors:95

The discount rate applying in the real estate market;

The earnings in any interim use;

The way in which the highest and best use of the land is expected to change in the

future; and

The property tax rate.

94 Marshal Valuation Service. (2007). Life Expectancy Guidelines, February 2007. Section 97, page 6. Los Angeles,

CA. 95 Shoup, D. C. (1970). The optimal timing of urban land development. Papers in Regional Science, 25(1), 33-44.

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Lower costs of borrowing tend to drive new investment. However, the location of new

development depends on local market demand for new structures. Without that demand no

new development occurs.

Land must also be available for development. Redevelopment occurs when the value of the

structures on the land approaches zero; ie, when the parcel most nearly approximates

competing vacant land. This occurs when the earnings of the current use are very low, such as

the case for agricultural land. The reliable rents from single family detached homes means only

non‐habitable structures are likely to be redeveloped.

Transit cannot make the market. Development near transit responds to zoning and regulatory

changes made in response to transit, rather than responding to the transit itself96. When

market and regulatory conditions are right, development can occur very rapidly, in some cases

preceding the opening of transit operations.

Finally, property taxes influence redevelopment. For an investor, property taxes effectively

reduce the income from a property. The lower the income of a property’s current use, the more

likely it will be redeveloped. Under this logic, effects that increase property values or property

taxes near transit (such as Transit Benefit Districts) may help spur redevelopment.

96 Chatman, D. G. (2013). Does TOD need the T? On the importance of factors other than rail access. Journal of the American Planning Association, 79(1), 17‐31.

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Figure 8‐18: Study Area Land Uses Circa 2015/2016

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8.5 Environmental Quality (Vehicle Emissions)

Vehicle emissions refer to a range of pollutants. The criteria for pollutants for most air quality

analyses includes volatile organic compounds (VOC), carbon monoxide (CO), nitrogen oxides

(NOx), sulfur dioxide (SO2 ), particulate matter under 10 microns in diameter (PM10), particulate

matter under 2.5 microns in diameter (PM2.5), methane (CH4), and nitrogen dioxide (NO2).

Neither carbon dioxide (CO2) or fuel consumption were included in these calculations.

8.5.1 Methods&Data

8.5.1.1 VehicleMilesTraveled

Emissions were estimated by multiplying VMT by emissions per mile traveled. The total amount

of VMT on the BRT alignment and other roads in the project area were estimated by multiplying

the AADT by road mileage. Figure 8‐19 shows the estimated VMT by facility type (arterial or

freeway) for the BRT alignment, Diversion Corridors, and the rest of Utah County. The method

has been validated by comparing VMT estimates using this method for 2011 to official counts

for 2011.97

Figure 8‐19: VMT by Facility Type and Geography

The BRT alignment consists of over 10 miles of some of the highest volume roads in Utah

County. Correspondingly, the total VMT on those roads is a very large number, equal to about

1/3 of the arterial volumes on the diversion corridors. Of the diversion corridors, I‐15

represents about half the volume, a relationship that holds for I‐15 and Utah County as a whole.

Different facilities have different shares of VMT by vehicle classes. The share of VMT by Vehicle

Type data was for Utah County98 for 2014. It was expanded by fuel type using the ratios from

GREET for MOVES.99 UTA does not currently have any gasoline buses, and is in the process of

97 Mountainland MPO (2015). 2040 Regional TRANSPORTATION PLAN: Appendix A, CONFORMITY DETERMINATION REPORT. Retrieved from: http://mountainland.org/img/transportation/TransPlan40/Appendix%20A%20‐%20AQ%20Conformity%20Report.pdf (May 30, 2017). 98 Provided by Kip Billings at WFRC, 18 January 2017. Data is input data for the travel model. 99 Cai, H., Burnham, A. & Wang, M. (2013). Updated Emission Factors of Air Pollutants from Vehicle Operations in GREET Using MOVES. Retrieved from: https://greet.es.anl.gov/publication‐vehicles‐13, table 24

Row Labels Arterial Freeway Grand Total

Alignment 400,985 ‐ 400,985

Diversion Corridors 1,245,436 1,404,757 2,650,193

Utah County, Remainder 4,986,620 4,662,477 9,649,097

Grand Total 6,633,041 6,067,234 12,700,275

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replacing all diesel buses with CNG buses. The BRT buses are anticipated to be CNG/clean diesel

buses. A CNG bus produces less than 1% as much VOC, CO, NOx as a diesel vehicle, and only

about two thirds as much PM10 and PM2.5. UTA currently has over 400 buses, of which 65%

are currently clean diesel or CNG100. As of March 2016, UTA had 47 CNG buses101. For initial

conditions, a service miles mix matching this fleet mix was used. VMT for vehicle/fuel type for

the BRT alignment, Diversion Corridors, and the Remainder of Utah County are presented in

Figure 8‐20.

Figure 8‐20: Share of VMT by Vehicle/Fuel Type and Geography

8.5.1.2 EmissionsperVMT

Mobile emission were estimated using emissions by vehicle class and fuel type as inputs into

the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model

for the Environmental Protection Agency (EPA) Motor Vehicle Emissions Simulator (MOVES).

100 Piellisch, R. (2016, January 5). Utah Transit Opens CNG Fueling. Fleets and Fuels. Retrieved May 20, 2017, from http://www.fleetsandfuels.com/fuels/cng/2016/01/utah‐transit‐opens‐cng‐fueling‐facility/ 101 Utah Transit Authority (2016). Transit and Air Quality. Retrieved from: https://www.rideuta.com/‐/media/Files/Publications/Transit_and_Air_Quality_Facts_2016_p.ashx?la=en, on May 30, 2017.

Alignment

Arterial Arterial Freeway Arterial Freeway

Combination long‐haul trucks diesel 23,270 72,274 81,520 304,395 284,608 766,067

Combination short‐haul trucks diesel 7,759 24,098 27,181 101,493 94,895 255,426

Combination short‐haul trucks gasoline ‐ ‐ ‐ ‐ ‐ ‐

Intercity buses diesel 369 1,145 1,292 4,823 4,510 12,139

Light commercial trucks diesel 3,431 10,656 12,019 42,292 39,543 107,942

Light commercial trucks gasoline 29,276 90,931 102,563 360,896 337,436 921,102

Motor homes diesel 399 1,240 1,398 5,222 4,882 13,141

Motor homes gasoline 399 1,240 1,398 5,222 4,882 13,141

Motorcycles gasoline 801 2,487 2,805 9,871 9,229 25,193

Passenger cars diesel 693 2,152 2,428 8,542 7,987 21,801

Passenger cars gasoline 180,344 560,139 631,794 2,223,142 2,078,632 5,674,051

Passenger trucks diesel 2,991 9,291 10,480 36,875 34,478 94,116

Passenger trucks gasoline 125,626 390,188 440,103 1,548,624 1,447,959 3,952,501

Refuse trucks diesel 877 2,724 3,073 11,473 10,727 28,874

Refuse trucks gasoline ‐ ‐ ‐ ‐ ‐ ‐

School buses diesel 1,916 5,950 6,711 25,059 23,430 63,066

School buses gasoline 135 419 473 1,765 1,650 4,441

Single‐unit long‐haul trucks diesel 826 2,567 2,895 10,810 10,107 27,206

Single‐unit long‐haul trucks gasoline 354 1,100 1,241 4,633 4,332 11,660

Single‐unit short‐haul trucks diesel 14,550 45,192 50,973 190,332 177,959 479,005

Single‐unit short‐haul trucks gasoline 6,228 19,343 21,817 81,464 76,169 205,020

Transit buses CNG 99 306 345 1,290 1,206 3,247

Transit buses diesel 447 1,388 1,566 5,846 5,466 14,713

Transit buses gasoline 294 912 1,029 3,843 3,593 9,671

400,985 1,245,436 1,404,757 4,986,620 4,662,477 12,700,275

Utah County, RemainderDiversion Corridors

FuelVehicle Type Grand Total

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Different classes of vehicles/fuel types have different operational characteristics, and therefore

different emissions. A common model year will be used for all analyses, in order to determine

differences in emissions due to change in VMT, rather than improving because fleet emissions

are improving. For emissions estimates, 2018 was used. As the model data for VMT shares are

provided only in five‐year increments,102 data for 2018 was interpolated from 2015 & 2020 VMT

shares using a linear relationship. The grams per mile rate by Vehicle/Fuel type103 for the 2018

model year is shown in Figure 8‐21. Colored databars are used to call attention to vehicle/fuel

type that produces the most of each pollutant.

Figure 8‐21: Emissions in Grams per Mile by Vehicle and Fuel Type

Per mile, Refuse Trucks (gasoline) produce the most VOC, NOx, and SO2. School Buses (gasoline

produce the most CO and N2O. Combination long‐haul trucks produce the most Nitrous Oxide,

PM2.5, and CH4. This can be attributed to their operation pattern, which involves repeated

acceleration and braking. Gasoline transit buses also produce substantial emissions.

102 Cai, H., Burnham, A. & Wang, M. (2013). Updated Emission Factors of Air Pollutants from Vehicle Operations in GREET Using MOVES. Retrieved from: https://greet.es.anl.gov/publication‐vehicles‐13, table 24 103 Cai, H., Burnham, A. & Wang, M. (2013). Updated Emission Factors of Air Pollutants from Vehicle Operations in GREET Using MOVES. Retrieved from: https://greet.es.anl.gov/publication‐vehicles‐13, tables 2‐23

Vehicle Fuel VOC CO NOx S02 PM10 PM2.5 CH4 NO2

Combination long‐haul trucks diesel 0.4051 1.6523 4.5835 0.0149 0.14497 0.08604 0.4635 0.002

Combination short‐haul trucks diesel 0.0926 0.4301 1.4192 0.0135 0.12067 0.06724 0.0472 0.0021

Combination short‐haul trucks gasoline 0 0 0 0 0 0 0 0

Intercity buses diesel 0.0949 0.6535 1.4222 0.0119 0.16566 0.08413 0.0567 0.0024

Light‐duty commercial trucks diesel 0.0784 1.2486 0.9698 0.0045 0.04802 0.02671 0.0936 0.0029

Light‐duty commercial trucks gasoline 0.2966 6.1397 0.4245 0.0054 0.05193 0.03053 0.027 0.0084

Motor homes diesel 0.0816 0.9402 0.9892 0.0073 0.08592 0.04161 0.0671 0.0026

Motor homes gasoline 1.5606 33.8175 3.3031 0.0143 0.07828 0.03418 0.0165 0.0067

Motorcycles gasoline 1.4575 13.0641 0.677 0.0056 0.08033 0.07143 0.0602 0.0071

Passenger cars diesel 0.0722 2.7352 0.2324 0.002 0.02804 0.01434 0.0927 0.0007

Passenger cars gasoline 0.1682 2.8611 0.12 0.0042 0.03322 0.01862 0.0111 0.0044

Passenger trucks diesel 0.077 1.32 0.942 0.0045 0.04653 0.02642 0.0916 0.0028

Passenger trucks gasoline 0.2725 4.9144 0.3088 0.0054 0.05213 0.03093 0.025 0.0078

Refuse trucks diesel 0.0932 0.6575 1.3056 0.0115 0.15473 0.07711 0.0586 0.0026

Refuse trucks gasoline 1.7536 29.4382 4.5224 0.0225 0.07143 0.03062 0.015 0.0061

School buses diesel 0.1129 2.9455 1.1158 0.0065 0.10977 0.05045 0.1147 0.0044

School buses gasoline 1.6931 85.7948 2.7719 0.0132 0.08018 0.04147 0.0856 0.0242

Single‐unit long‐haul trucks diesel 0.0749 0.9398 0.8931 0.0066 0.10298 0.04596 0.0707 0.0029

Single‐unit long‐haul trucks gasoline 0.965 30.6937 3.0315 0.0132 0.07968 0.03187 0.0261 0.0089

Single‐unit short‐haul trucks diesel 0.0772 1.0199 0.9388 0.007 0.1039 0.04669 0.0755 0.0031

Single‐unit short‐haul trucks gasoline 0.9124 33.0264 3.1204 0.014 0.08008 0.03208 0.04 0.0108

Transit buses CNG 2.1E‐05 0.019175 0.00206 0.0089 0.06865 0.03257 0.0642 0.00019

Transit buses diesel 0.0868 1.0895 1.2096 0.0089 0.08914 0.05222 0.0642 0.0029

Transit buses gasoline 1.6719 39.2963 3.537 0.0176 0.0688 0.0321 0.0338 0.01

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To determine total emissions, VMT was apportioned in accordance to the lifetime VMT for each

vehicle class and fuel type for model year 2018. The apportioned VMT was then multiplied by

the emissions rate for each vehicle/fuel class to get total emissions for each vehicle class, which

was then summed to obtain total emissions for the BRT alignment and the study area.


Figure 8‐22 shows the results broken out by vehicle and fuel types as percentages, to

demonstrate which types of vehicles in the anticipated 2018 fleet will generate the most

pollution. The cells in Figure 8‐22 have been colored to call out the largest contributor in each

category, with red being the largest and green the least.

Figure 8‐22: Share of Emissions by Vehicle Type

As expected, emissions are largely proportional to the share of VMT; passenger cars and trucks,

which represent about 75 percent of the VMT traveled, are the major share of most emissions.

Combination long‐haul trucks emit a large share of the total NOx and CH4. Transit buses emit

only a miniscule share of emissions.

Total emissions in grams for the BRT corridor is quantified in Figure 8‐24. It shows emissions by

the share of each pollutant emitted by vehicle type to identify which vehicle/fuel types are the

most severe emitters of each type of pollutant. The cells have been colored‐coded to call out

the largest contributor in each category, with red being the largest and green the least.


Combination long‐haul trucks diesel 10% 2% 44% 15% 16% 17% 55% 2%

Combination short‐haul trucks diesel 1% 0% 5% 5% 4% 4% 2% 1%

combination short‐haul trucks gasoline 0% 0% 0% 0% 0% 0% 0% 0%

Intercity buses diesel 0% 0% 0% 0% 0% 0% 0% 0%

Light‐duty commercial trucks diesel 0% 0% 1% 1% 1% 1% 2% 0%

Light‐duty commercial trucks gasoline 9% 11% 5% 7% 7% 7% 4% 11%

Motor homes diesel 0% 0% 0% 0% 0% 0% 0% 0%

Motor homes gasoline 1% 1% 1% 0% 0% 0% 0% 0%

Motorcycles gasoline 1% 1% 0% 0% 0% 0% 0% 0%

Passenger cars diesel 0% 0% 0% 0% 0% 0% 0% 0%

Passenger cars gasoline 32% 31% 9% 33% 28% 28% 10% 35%

Passenger trucks diesel 0% 0% 1% 1% 1% 1% 1% 0%

Passenger trucks gasoline 36% 37% 16% 29% 31% 32% 16% 44%

Refuse trucks diesel 0% 0% 0% 0% 1% 1% 0% 0%

Refuse trucks gasoline 0% 0% 0% 0% 0% 0% 0% 0%

School buses diesel 0% 0% 1% 1% 1% 1% 1% 0%

School buses gasoline 0% 1% 0% 0% 0% 0% 0% 0%

Single‐unit long‐haul trucks diesel 0% 0% 0% 0% 0% 0% 0% 0%

Single‐unit long‐haul trucks gasoline 0% 1% 0% 0% 0% 0% 0% 0%

Single‐unit short‐haul trucks diesel 1% 1% 6% 4% 7% 6% 6% 2%

Single‐unit short‐haul trucks gasoline 6% 12% 8% 4% 2% 2% 1% 3%

Transit buses CNG 0% 0% 0% 0% 0% 0% 0% 0%

Transit buses diesel 0% 0% 0% 0% 0% 0% 0% 0%

Transit buses gasoline 1% 1% 0% 0% 0% 0% 0% 0%

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Figure 8‐24: Average Annual Emissions by Vehicle Type for BRT Corridor in Grams

As the table shows, emissions from passenger cars and trucks are the primary sources of VOCs

and NOx, both of which are precursor chemicals to ozone. Passenger cars and trucks are also

the main emitters of PM2.5 (with combination long‐haul trucks the next most severe). The

Provo‐Orem Metropolitan Area is currently in Serious Non‐Attainment for PM2.5.104

For the analysis of emissions, the study area consists of the diversion corridors. Figure 8‐23

shows the emissions by vehicle/fuel type for each category of emissions. The cells have been

colored‐coded to call out the largest contributor in each category, with red being the largest

and green the least.

Figure 8‐23: Average Annual Emissions by Vehicle Type for Diversion Corridors in Grams

104 Environmental Protection Agency (May 2015) Determinations of Attainment by the Attainment Date, Determinations of Failure To Attain by the Attainment Date and Reclassification for Certain Nonattainment Areas for the 2006 24‐Hour Fine Particulate Matter National Ambient Air Quality Standards


Combination long‐haul trucks diesel 9,427 38,449 106,657 347 3,373 2,002 10,786 47

Combination short‐haul trucks diesel 718 3,337 11,011 105 936 522 366 16

Combination short‐haul trucks gasoline ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐

Intercity buses diesel 35 241 524 4 61 31 21 1

Light‐duty commercial trucks diesel 269 4,284 3,327 15 165 92 321 10

Light‐duty commercial trucks gasoline 8,683 179,748 12,428 158 1,520 894 790 246

Motor homes diesel 33 375 395 3 34 17 27 1

Motor homes gasoline 623 13,499 1,318 6 31 14 7 3

Motorcycles gasoline 1,167 10,461 542 4 64 57 48 6

Passenger cars diesel 50 1,895 161 1 19 10 64 0

Passenger cars gasoline 30,334 515,983 21,641 757 5,991 3,358 2,002 794

Passenger trucks diesel 230 3,949 2,818 13 139 79 274 8

Passenger trucks gasoline 34,233 617,378 38,793 678 6,549 3,886 3,141 980

Refuse trucks diesel 82 577 1,145 10 136 68 51 2

Refuse trucks gasoline ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐

School buses diesel 216 5,643 2,137 12 210 97 220 8

School buses gasoline 228 11,574 374 2 11 6 12 3

Single‐unit long‐haul trucks diesel 62 777 738 5 85 38 58 2

Single‐unit long‐haul trucks gasoline 342 10,871 1,074 5 28 11 9 3

Single‐unit short‐haul trucks diesel 1,123 14,840 13,660 102 1,512 679 1,099 45

Single‐unit short‐haul trucks gasoline 5,682 205,675 19,433 87 499 200 249 67

Transit buses CNG 0 2 0 1 7 3 6 0

Transit buses diesel 39 487 541 4 40 23 29 1

Transit buses gasoline 491 11,544 1,039 5 20 9 10 3

TOTAL 94,068 1,651,585 239,757 2,327 21,432 12,095 19,589 2,247

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The results suggest that transit buses are minimal contributors to overall pollution. Switching

automobile miles to transit bus miles should significantly reduce pollution; this is especially true

for CNG buses.


The same VMT mix was used for all arterials in Utah County and the BRT alignment, as no

specific VMT mix exists for the BRT alignment. Nor is the VMT mix an exact match to actual

conditions. The share of VMT mileage data used is for 2014, rather than 2018. While it provides

an inferior match in terms of the modeling year, it provides a better match for mileage in the

geography than using GREET/MOVES defaults. Figure 8‐24 shows the differences between the

two rates; Utah County has more light trucks than the national average, and fewer passenger

cars. It also has more combination long‐haul trucks. UTA’s exact fleet mix, fleet mix in Utah

County, or mix of service miles by fuel type are unknown.


Combination long‐haul trucks diesel 62,302 254,115 704,917 2,292 22,296 13,232 71,284 308

Combination short‐haul trucks diesel 4,748 22,055 72,775 692 6,188 3,448 2,420 108

Combination short‐haul trucks gasoline ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐

Intercity buses diesel 231 1,593 3,466 29 404 205 138 6

Light‐duty commercial trucks diesel 1,778 28,312 21,990 102 1,089 606 2,122 66

Light‐duty commercial trucks gasoline 57,390 1,187,991 82,138 1,045 10,048 5,907 5,224 1,625

Motor homes diesel 215 2,480 2,610 19 227 110 177 7

Motor homes gasoline 4,117 89,216 8,714 38 207 90 44 18

Motorcycles gasoline 7,713 69,137 3,583 30 425 378 319 38

Passenger cars diesel 331 12,526 1,064 9 128 66 425 3

Passenger cars gasoline 200,483 3,410,239 143,032 5,006 39,596 22,194 13,230 5,245

Passenger trucks diesel 1,522 26,097 18,624 89 920 522 1,811 55

Passenger trucks gasoline 226,254 4,080,384 256,394 4,484 43,283 25,681 20,757 6,476

Refuse trucks diesel 540 3,811 7,568 67 897 447 340 15

Refuse trucks gasoline ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐

School buses diesel 1,429 37,293 14,127 82 1,390 639 1,452 56

School buses gasoline 1,510 76,496 2,471 12 71 37 76 22

Single‐unit long‐haul trucks diesel 409 5,133 4,878 36 562 251 386 16

Single‐unit long‐haul trucks gasoline 2,259 71,847 7,096 31 187 75 61 21

Single‐unit short‐haul trucks diesel 7,424 98,078 90,279 673 9,991 4,490 7,260 298

Single‐unit short‐haul trucks gasoline 37,554 1,359,350 128,434 576 3,296 1,320 1,646 445

Transit buses CNG 0 12 1 6 45 21 42 0

Transit buses diesel 256 3,218 3,573 26 263 154 190 9

Transit buses gasoline 3,246 76,294 6,867 34 134 62 66 19

TOTAL 621,713 10,915,679 1,584,603 15,377 141,646 79,936 129,471 14,853

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Figure 8‐24: Share of Vehicles by Vehicle and Fuel Type

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9 System Effects on Roadway Network This section provides information on the initial conditions of the roadway network, prior to the

construction of the Provo‐Orem BRT. Baseline conditions for Annual Average Daily Traffic

(AADT) are documented. Initial conditions for factors that might affect AADT, such as new

development, new transportation infrastructure, and student enrollment, are also covered.

Data on parking supply and occupancy was collected. Finally, baseline data for other factors

that might be affected by the new BRT (automobile crash rates and vehicle emissions) were

collected. This section thus contains the following sub‐sections:

1. Traffic Counts

2. New Development

3. New Transportation Infrastructure

4. Student Enrollment

5. Parking Supply & Occupancy

6. Crash Rates

9.1 Traffic Counts

This section details the methods and data used for traffic counts. Counts (AADT) for the

alignment, diversion corridors, and Utah County are provided as both graphs and tables.

Additionally, counts for ‘multi‐corridor screen lines are shown. Data limitations regarding count

frequency and coverage are then discussed.

9.1.1 Methods&Data

Historical traffic counts of AADT were obtained for at least five years prior to BRT construction

for all roads within the study area. The point counts of AADT were assigned to road segments.

Road segments along a route were summed to provide corridor counts. Counts from multiple

parallel corridors were summed to provide counts for screen lines. Screen lines (i.e., cut‐lines)

are lines cutting across segments of numerous corridors intended to measure the sum total of

all traffic flows across the entire study area. Projections on future AADT were made using a

linear estimator for the previous five years to each year on a compounding basis. This was used

in preference to average annual compound change because it is less sensitive to year‐specific

counts. Because the counts were proportional to the number of road segments, the counts

were divided by the number of road segments to produce an average count for each corridor or

screen line. Because the BRT alignment does not exactly match roads with AADT counts, the

segments of roads with counts best approximating the BRT alignment were combined to

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provide an equivalent. The source data was UDOT’s Traffic on Utah Highways, which is

distributed through their Open Data Platform.105

To validate these counts, additional data from the web based Automated Traffic Signal

Performance Measurement System (SPMs) was used. Data for all intersections in the study area

were requested106, but this request was non‐feasible due to the magnitude of the associated

data. Data for signals along the BRT alignment were provided107 in 15 minute bins. No signal

data is available prior to 2012; 2012‐2013 data are only available for signals 6315 and 6316.

Data for most signals are available in 2014 and 2015 (excepting signals 6427 and 6436). For

some intersections, the detector channels that count vehicles either had not started operation,

or were out of operation for part of the year. As it furnished the most complete data,

intersection counts from 2015 were used. Many of the channels were available lacked the

relevant channels to compare to the UDOT AADT count. The data that remained available is

shown in Figure 9‐1.

Figure 9‐1: Intersections along BRT Alignment

To compare with AADT along the BRT alignment, an AADT analogue was created. This was

created by the available 15 minutes counts for each channel to get an average count, and then

105 Sellers, Adrian (March 2017). AADT (Open Data). Utah Department of Transportation. Retrieved May 30, 2017, from http://www.udot.utah.gov/main/f?p=100:pg:0::::V,T:,528 106 Signal #’s: 6315, 6316, 6317, 6318, 6319, 6320, 6321, 6322, 6324, 6324, 6328, 6338, 6401, 6402, 6403, 6404, 6405, 6406,6407, 6408, 6409, 6410, 6411. 6417, 6427, 6435, 6436, 6619, 6623, 6625, 6627, 6628, 6633, 6634, 6635, 6651, 6652, 6654, 6655 107 Jamie Mackey, P.E., PTOE. Statewide Signal Engineer, <[email protected]>

Signal Number Intersection 2015 MISSING DATES Relevant Channels

6315 Geneva Road @ 1000 South NA All

6316 Geneva Road @ University Parkway Ends July 16 Missing

6317 University Parkway @ I‐15 SPUI NA Missing

6328 University Pkwy @ 680 E (University Pl) NA Missing

6401 University Avenue @ 1860 South End July 29 Missing

6402 University Avenue @ East Bay Boulevard Ends Feb 26 All

6404 University Avenue @ 920 South NA Missing

6405 University Avenue @ 300 South NA Missing

6407 University Avenue @ Center Street Ends Dec. 22 Missing

6408 University Avenue @ 100 North Ends Dec. 22 Missing

6417 University Avenue @ University Parkway NA All

6427 200 S @ University Ave (US‐189) No 2015 Data NA

6436 University Parkway @ 2230 North No 2015 Data NA

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multiplying that count by four (4 counts per hour) and by 24 (24 hours in a day). The channel

counts were then summed to create ‘leg’ counts in the following way: For the north leg, all thru

movements for northbound or southbound traffic was summed. Right‐turning east‐bound

traffic and left‐turning westbound traffic was then summed and added to that total. This

procedure was then repeated for all legs of an intersection.


9.1.2.1 Alignment&DiversionCorridors

To detect changes in corridor traffic over time, the averaged AADT counts for all line segments

of the alignment and diversion corridors from 2010—2014 are shown in Figure 9‐2.

Figure 9‐2: Estimated AADT on Diversion Corridors 2010—2015

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Averaged counts are the sum of counts on all segments, divided by the number of segments. I‐

15 is omitted for reasons of magnitude; Figure 9‐9 shows the relative magnitude of traffic

volumes in the study area.

Figure 9‐3 shows the data table for the preceding graph, and includes the 2010‐2015 changes

as average annual numeric and percentage changes, as well as linear estimates of numeric and

percentage changes. Average annual changes have been included for familiarity, but the linear

estimate is more robust, as it includes information from all five years. Numeric and percentage

changes have been color‐coded to highlight significant changes. For numeric changes, a red‐

blue spectrum indicates the magnitude of changes; red is increase, blue is decrease. For

percentage changes, a red‐yellow‐green spectrum indicates the magnitude of changes; green is

positive, red is negative.

Figure 9‐3: Estimated AADT by Diversion Corridor 2010‐2015

Overall, AADT is increasing across Utah County. Orem 800 North, I‐15, and Geneva Road saw

increases of over 2 percent per year. However, only on Orem 800 North did AADT increase at

percentage rate faster than the rest of Utah County. The largest numeric increases occur on

Orem 800 North and I‐15. As part of the I‐15 Core project, I‐15 was rebuilt between 2012 and

2015. The rebuild included widening the freeway by two lanes in each direction, the addition of

# % # %

BRT Alignment 13,944 13,520 13,475 14,283 14,795 15,678 347 2.5% 380 2.7%

Freedom Boulevard 12,939 12,909 12,494 12,183 12,317 12,931 (2) 0.0% (61) ‐0.5%

Geneva Road (Highway 114) 11,679 11,410 12,383 12,318 12,454 12,989 262 2.2% 275 2.4%

HWY 189 26,716 27,001 26,108 26,048 27,481 28,812 419 1.6% 339 1.3%

I‐15 118,218 112,434 110,045 119,128 124,208 133,696 3,096 2.6% 3,480 2.9%

Orem 1200 South 6,202 6,230 6,103 5,422 5,480 5,717 (97) ‐1.6% (153) ‐2.5%

Orem 800 East 13,588 13,546 13,275 13,061 13,205 13,772 37 0.3% (9) ‐0.1%

Orem 800 N (Highway 52) 22,848 22,961 25,468 26,114 27,229 28,558 1,142 5.0% 1,200 5.3%

Orem Center Street 22,356 22,298 21,852 20,911 21,510 22,506 30 0.1% (73) ‐0.3%

Provo 2200 N 16,040 15,588 14,733 14,365 14,523 16,225 37 0.2% (75) ‐0.5%

Provo 700 N 11,883 11,300 7,905 7,708 7,793 8,125 (752) ‐6.3% (843) ‐7.1%

Provo 900 E 19,470 19,325 18,351 17,893 18,090 18,868 (121) ‐0.6% (205) ‐1.1%

Provo Center Street 27,240 26,778 26,480 26,545 27,318 28,583 269 1.0% 240 0.9%

State Street (HWY 89) 30,390 29,937 28,678 29,683 30,990 32,509 424 1.4% 422 1.4%

University Ave (HWY 189) 30,948 30,414 30,561 29,470 31,092 32,874 385 1.2% 302 1.0%

University Parkway 32,565 32,168 31,428 31,463 32,643 34,347 356 1.1% 296 0.9%

Utah County, Remainder of 11,062 11,201 11,201 11,586 12,188 12,988 385 3.5% 371 3.4%

Utah County 13,782 13,817 13,744 14,114 14,786 15,716 387 2.8% 370 2.7%

Changes 2010‐2015

Street 2010 2011 2012 2013 2014 2015 Linear EstimateAverage Annual

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an express lane (HOV) from Orem to Spanish Fork AADT on I‐15, and the rebuilding of 10

interchanges.108

Several of the diversion corridors saw lower AADT over time: Orem 1200 South (near UVU) saw

dropping AADT over time. This may represent diversion to University Parkway. Provo 700 N saw

notable declines, both numerically and as a percentage, but this decline was constrained to the

eastern third of that route (between Provo 700 East and 900 East). AADT on the BRT alignment

is increasing at percentage rates approaching that of Utah County as a whole, but without

major numeric changes. The MAG Long Range Transportation Plan (LRTP) calls for widening

Orem Center Street to six lanes. Current AADT on that route is flat, which may indicate either a

lack of demand, or that the route is operating at capacity.

Figure 9‐4 provides estimated and forecast AADT for 2010‐2015 to compare actual future

accounts against.

Figure 9‐4: Estimated and Forecast AADT 2010‐2020

Projections on future AADT were made using a linear estimator for the previous five years to

each year on a compounding basis. This makes it possible to compare trend forecasts to actual

future year counts and detect deviations from the expected AADT trend for each road. These

projections are not travel model numbers but merely trend extrapolation. They are not capacity

constrained and should be used with care.

108 Mountainland Association of Governments. (n.d.). TransPlan40 ‐ Regional Transportation Plan [2015‐2040 Plan for the Provo/Orem Metropolitan Area. Retrieved May 20, 2017, from http://mountainland.org/img/transportation/TransPlan40/TransPlan40.pdf

Street 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

BRT Alignment 13,944 13,520 13,475 14,283 14,795 15,678 16,105 16,545 16,996 17,459 17,935

Freedom Boulevard 12,939 12,909 12,494 12,183 12,317 12,931 12,871 12,810 12,750 12,690 12,631

Geneva Road (Highway 114) 11,679 11,410 12,383 12,318 12,454 12,989 13,294 13,607 13,927 14,255 14,590

HWY 189 26,716 27,001 26,108 26,048 27,481 28,812 29,177 29,548 29,922 30,302 30,686

I‐15 118,218 112,434 110,045 119,128 124,208 133,696 137,632 141,683 145,854 150,147 154,567

Orem 1200 South 6,202 6,230 6,103 5,422 5,480 5,717 5,576 5,438 5,304 5,173 5,045

Orem 800 East 13,588 13,546 13,275 13,061 13,205 13,772 13,763 13,754 13,744 13,735 13,726

Orem 800 N (Highway 52) 22,848 22,961 25,468 26,114 27,229 28,558 30,057 31,636 33,297 35,046 36,886

Orem Center Street 22,356 22,298 21,852 20,911 21,510 22,506 22,432 22,359 22,286 22,213 22,141

Provo 2200 N 16,040 15,588 14,733 14,365 14,523 16,225 16,149 16,073 15,997 15,922 15,847

Provo 700 N 11,883 11,300 7,905 7,708 7,793 8,125 7,549 7,013 6,515 6,053 5,624

Provo 900 E 19,470 19,325 18,351 17,893 18,090 18,868 18,669 18,472 18,278 18,085 17,895

Provo Center Street 27,240 26,778 26,480 26,545 27,318 28,583 28,834 29,088 29,344 29,603 29,864

State Street (HWY 89) 30,390 29,937 28,678 29,683 30,990 32,509 32,960 33,417 33,881 34,351 34,827

University Ave (HWY 189) 30,948 30,414 30,561 29,470 31,092 32,874 33,195 33,519 33,847 34,177 34,511

University Parkway 32,565 32,168 31,428 31,463 32,643 34,347 35,498 36,687 37,917 39,187 40,500

Utah County, Remainder of 11,062 11,201 11,201 11,586 12,188 12,988 13,423 13,873 14,338 14,818 15,315

Utah County 13,782 13,817 13,744 14,114 14,786 15,716 16,138 16,571 17,016 17,472 17,941

ForecastEstimates

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9.1.2.2 ScreenLines

The screen line sum counts on all roads, not just diversion corridors, controlling for diversion

onto smaller roads. The averaged AADT counts for screen lines are shown in Figure 9‐5 as a

graph. A reference map of screen line locations is shown in Figure 9‐8.

Figure 9‐5: Estimated AADT by Screen Line 2010—2015

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The counts for screen lines and the rate of growth/decline in AADT over time is shown in Figure

9‐6. The name of each screen line gives its approximate location. Numeric and percentage

changes have been color‐coded to highlight significant changes. For numeric changes, a red‐

blue spectrum indicates the magnitude of changes; red is increase and blue is decrease. For

percentage changes, data bars indicate magnitude of change; red is negative and green is

positive.

Figure 9‐6: Estimated AADT and Changes by Screen Line 2010‐2015

Growth along the screen lines ranges from ‐0.4 percent to 3.0 percent. Traffic crossing the

North‐South screen line across BYU declines. The screen line at Provo 1140 South (crossing both

University Avenue and State Street) shows the largest percentage of growth. Numerically, the

screen line at Provo 1800 South shows the largest charge, which is likely because it intersects

both I‐15 and State Street.

9.1.2.3 AutomaticSignalPerformanceMeasuresSystem

As mentioned previously, there were only 3 signals with data for 2015 and channels relevant for

comparison with AADT segments. The totals for those intersections are shown in Figure 9‐7.

Figure 9‐7: Traffic Signal Counts

# % # %

Bluff 23,008 22,308 22,235 23,568 24,413 25,869 572 2.5% 627 2.6%

BYU Cordon 17,590 17,590 16,616 16,803 17,402 18,221 126 0.7% 79 0.8%

East‐West at about 1100 S. in Orem 21,696 21,147 20,427 21,661 22,461 23,884 438 2.0% 461 2.1%

East‐West at about 1140 S. in Provo 17,562 18,575 18,195 18,358 19,247 20,185 525 3.0% 437 2.9%

East‐West at about 1500 S. In Orem 23,964 23,171 21,084 22,882 23,802 25,272 262 1.1% 292 1.2%

East‐West at about 1800 S. in Provo 61,080 58,543 58,073 60,503 62,810 66,623 1,109 1.8% 1,227 1.9%

East‐West at about 200 N. in Orem 26,633 25,771 25,996 24,532 25,442 27,049 83 0.3% (11) 0.3%

East‐West, across BYU Campus 19,631 20,487 19,781 19,511 20,102 21,029 280 1.4% 159 1.4%

East‐West, south of BYU campus 18,997 19,312 18,912 20,095 20,799 21,773 555 2.9% 558 2.9%

East of I‐15 in Orem 17,600 18,443 18,256 18,341 19,034 19,945 469 2.7% 388 2.6%

East of I‐15 in Provo 11,669 11,449 11,608 11,592 12,005 12,693 205 1.8% 193 1.8%

North‐South, across BYU Campus 15,066 14,446 14,003 13,653 13,803 14,783 (57) ‐0.4% (106) ‐0.4%

Changes 2010‐2015

Average Annual Linear Estimate201520142013201220112010Cutlines

North East South West

6315 Geneva Road 1000 South 12,280 NA 12,280 NA 12,280

6402 University Avenue East Bay Boulevard 33,110 7,085 33,110 NA 24,435

6417 University Avenue University Parkway 27,040 17,810 32,475 40,870 29,549

North East South West

6315 Geneva Road 1000 South 6,244 4,972 5,965 5,490 30,193

6402 University Avenue East Bay Boulevard 26,622 7,094 29,828 8,721 45,748

6417 University Avenue University Parkway 20,887 16,537 17,909 13,086 43,027

Street Crossstreet

AADT Leg Volumes

AverageCrossstreetStreetSignal

AADT Analogue Leg Volumes

All MovementsSignal

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While there are areas of agreement, the traffic signal counts differ significantly from AADT

counts. This is likely because AADT counts are mid‐segment counts, while the traffic signal

provides point counts. As can be expected, signal counts are substantially lower, as they do not

count vehicle movements that either begin or end on the AADT segment. For a segment with a

high activity density, (jobs & homes) this can represent a large number of trips.

There is also a geographic mis‐match issue. The AADT count for the segment signal 6315 is on

extends from Orem Center Street to Orem 1200 South. The AADT count for the segment signal

6401 is on extends from Provo 600 south to I‐15. The segment for signal 6417 is relatively small,

but also has the highest activity density. Several of the intersection legs lack AADT counts; they

have been labeled ‘NA’.


For traffic counts, the data source was UDOT’s Traffic Statistics109 webpage (a time‐series of

traffic counts using a combination of permanent and spot counters). The differences between

counts at different points are reconciled to form segment‐level counts. Spot counts occur on a

three‐year cycle, and intermediary counts are interpolated from this data.110 The data requires

processing and was not immediately available; 2015 data became available only in early

February 2017. The data are available for all federal aid and state aid highways in the state of

Utah. A major limitation is that the data set only tracks the largest roads and so cannot track

diversion onto minor roads.

Not all roads have counts for all years for two reasons. First, only roads with relatively high

volumes are counted, and counts only begin when roads reach sufficient volumes. Second,

routes change. The closure of Campus Drive divided the previous UDOT route into two sections

that were renumbered as new routes. No counts were available for Campus Drive in 2015.

9.1.3.1 Wavetronix

Apart from permanent count stations, roadway counts of AADT are performed only every third

year. Consequently, additional data sources were sought to validate AADT counts. One of these

suggests data sources were WaveTronix ‘sidefire’ traffic counters, located in both Orem and

Provo. Shane Winters, Principal Engineer at Provo City, indicated that there is no historical data

o the detectors, saying:

109 Utah Department of Transportation ‐ Traffic Statistics. (n.d.). Retrieved May 20, 2017, from https://www.udot.utah.gov/main/f?p=100:pg:0::::V,T:,507 110 Traffic counts near Saratoga Springs are uncertain due to construction and changing conditions.

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“We are able to pull data from these devices, but only the data that can be stored on the

device themselves. Which is usually only a week or two at most of data storage, before

it begins to over right itself. We currently don’t have any current (last few months)

data.”111.…. “The data must be manually downloaded (as we currently do not have them,

or are interested in purchasing the current Wavetronix software to automate the

process)”… “When we need traffic data from these areas we will use these devices and

manually download the data, but we haven’t needed traffic data from these areas from

quite some time…..The data from these devices are stored on our TOC desktops… We

replaced our TOC desktop computers in late 2015 and all the data went with the old

computers as well as our Wavetronix software….the most recent data that was on the

old computers was from 2009 and we didn't feel that data was necessary to keep”.112

The detectors do exist, and despite construction, many are still operating. According to Shane

Winters, “We do have about 26 (16‐20 that are actually working)”.113 It is recommended that future

years of analysis reports should obtain and analyze this data.

Orem, Utah, also has a Wavetronix system, but many of the sensors are not currently online. Current

planning is to repair them, beginning in July 2017114. The Orem network is integrated into the UDOT

signal network.

111 Shane Winters, January. 3 2017, email. 112 Shane Winters, June. 1 2017, email. 113 Shane Winters, June. 1 2017, email. 114 Taylor Forbush, June 6, 2017, email.

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Figure 9‐8: Map of Diversion Corridors and Screen Lines

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Figure 9‐9: ‘Blood Vessel’ Map of 2015 AADT

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9.2 Trip Generation by New Development

This section covers the trips generated within the study area. All land uses within the study area

were assigned an ITE trip generation code, and the number of trips each parcel produced was

estimated. This is intended to provide an estimate of the total trips generated within the study

area, in order to control for changes in traffic volumes in the study area.

9.2.1 Methods&Data

The data used for land use was parcel‐level land use data provided by the Utah County

Assessor. Each parcel was assigned a land use type from the ITE Trip Generation Manual. The

assessor data does not differentiate between different types of commercial land uses. For

several thousand parcels, an ITE land use code was assigned based on the inspection of aerial

images and/or Google Street View. Total trips generated by parcels within the study area were

estimated using the average trip‐generation rates published in the ITE Trip Generation Manual.

The trip generation rates for each land use can be found in APPENDIX B.: ITE Trip Generation

Rates. Trips were estimated per thousand square foot (PER_KSF), per dwelling unit (PER_DU),

or per other unit (PER MISC). Vacant parcels use an invented ITE code of 1000. The average trip

rate is “a weighted average of the number of trip ends,” with “trip end” referring to a one‐way

movement from a point of origin to a point of destination.115


Due to the large number of parcels, and large number of ITE codes for parcels within the study

area, the resulting tables of trips are very large. Rather than present them here, a summary

table of trip generation by 1‐digit land use type is presented in Figure 9‐10. The Land Use Class

Code indicates the general three‐digit ITE code for the land uses in question and the related

Land Use Class. Parcel Count indicates the number of parcels in each land use class. Total Trips

is the sum of all trips for that land use class. The Acres column shows all acres for all parcels in

each land use class. The Trips per Acre column provides a measure of the relative “density” of

trip making by land use class.

115 ITE Technical Council Committee. (October 1976). Trip Generation. Traffic Engineering, 42‐47. Retrieved May 20, 2017, from https://nacto.org/docs/usdg/trip_generation_ite.pdf.

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Figure 9‐10: Trip Generation Totals by Land Use Class

While the number of trips is very large, so is the study area. The alignment is more than 10.5

miles long, and the area in proximity thousands of acres. The majority of trips are generated by

commercial land uses, both overall and on a trips‐per‐acre basis. Schools (high schools and

universities) also generate a considerable number of trips.

An estimation of trip ends within the study area cannot account for the total amount of traffic

in a study corridor, which contains a substantial number of through trips generated by uses

outside the study corridor. In the context of this study, the most important element is the

change in the total magnitude of trips generated. As long as the same trip generation rates are

applied in both the before and after land use data, the difference in trip generation should be

accounted for.

Figure 9‐11 shows a map of land uses using Land Based Classification Standard (LBCS),116 at the

1‐digit level. As shown, the primary land use is residential with institutional uses a close second.

Commercial uses situated along major roadways make up the majority of the remaining land

area.


This study relies on trip generation rates from the 9th edition of the ITE Trip Generation Manual.

The 9th edition included 5,500 data points spread over 172 different land uses.117 These rates

have been assumed to be reasonable. However, ITE trip generation studies represent an

accumulation of data going back decades. For some land uses, the trip generation rates have

experienced substantial changes. ITE trip generation rates have been used without controlling

116 American Planning Association. (n.d.). Land Based Classification Standard. Retrieved May 20, 2017, from https://www.planning.org/lbcs/ 117 Institute of Transportation Engineers. (n.d.). Trip Generation Publications. Retrieved May 20, 2017, from http://www.ite.org/tripgeneration/trippubs.asp

Land Use Class Code Land Use Class Parcel Count Total Trips Acres Trips per Acre

0 Rail Station 2 899 14 66

100 Utilities 1,499 59 878 0

200 Residential 10,852 132,717 2,283 58

400 Parks & Rec 16 172,147 212 812

500 Schools 454 342,406 1,612 212

600 Hospital 17 22,984 75 307

800 Commercial 1,356 5,583,186 1,145 4,874

Totals 14,196 6,254,398 6,219 1,006

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for effects associated for mixed‐use development.118 At some point in the next few years, ITE is

likely to publish a 10th edition of the manual, and trip generation rates for many land uses may

change. However, for the purposes of consistency, the 9th edition trip generation rates will

continue to be used in further analysis years.

The Utah County Assessor data contains limited land use information. Within the study area,

many parcels lack data and many parcels are missing data. Total number of units in apartment

complexes were not counted; instead, a range was provided. Total number of units was

estimated using the middle of the range of units provided.

118 Ewing, R., Greenwald, M., Zhang, M., & Walters, J. (September 2011). Traffic Generated by Mixed‐Use Developments—Six‐Region Study Using Consistent Built Environmental Measures. Journal of Urban Planning and Development, 137(3). Retrieved May 20, 2017, from http://ascelibrary.org/doi/abs/10.1061/(ASCE)UP.1943‐5444.0000068

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Figure 9‐11: Map of Study Area Land Use

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9.3 New Transportation Infrastructure

The Mountainland Association of Government’s fiscally constrained transportation plan

(“TransPlan”) calls for a series of improvements in the project area. They are shown in Figure

9‐12. The projects are color‐coded by phase, and symbolical‐coded by type. Only Phase 1

projects (colored red) are considered, as other phases are beyond the evaluation period.

Figure 9‐12: Project Area Transportation Improvements119

119 Mountainland Association of Governments. (n.d.). TransPlan40 ‐ Regional Transportation Plan [2015‐2040 Plan for the Provo/Orem Metropolitan Area. Retrieved May 20, 2017, from http://mountainland.org/img/transportation/TransPlan40/TransPlan40.pdf

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Presently, there are two interchanges within the project area. Two new interchanges have been

proposed for the project area:

#51: Half HOV interchange 800 South Orem and

#15: Full interchange 820 North Provo.

The half HOV interchange would substantially improve access to UVU from both I‐15 and

Wolverine Crossing. It would also provide a more direct route to UVU from the Orem

Intermodal Center and FrontRunner station. This route would also be less congested by

avoiding the I‐15/University Parkway interchange and associated University Parkway/Campus

Drive intersections. While identified as a Phase 1 project, it is also identified as unfunded.

The addition of a full interchange at 820 North in Provo can be expected to substantially affect

travel patterns. The proposed interchange lies between the one at University Parkway in Orem

and the one at Center Street in Provo.

The planned Lakeview Parkway is a two‐ to four‐lane roadway running from Geneva

Road/Orem 1400 South to I‐15/Provo 1860 South. The southern portion running east to west

from I‐15 to Provo Municipal Airport is currently under construction. It is shown on the map as

#16. It will affect traffic volumes on both Geneva Road and I‐15.

Project #22 is the widening of University Parkway between 800 East in Orem and University

Avenue in Provo. This project is part of the Provo‐Orem TRIP, and is currently underway. The

additional roadway capacity should induce a triple convergence effect, and traffic volumes

should be expected to increase. This increase will be contingent on Project #21, which is a

planned grade‐separated intersection at State Street and University Parkway. Project #21

should substantially increase capacity at that intersection, reducing congestion on both State

Street (Highway 89) and University Parkway. Traffic that has already diverged to alternate

routes and times will converge to these roads until the network again reaches equilibrium.

Project #19 widens Provo 820 North from Geneva Road to University Avenue, from two lanes to

four lanes. In combination with Project #15 (a new interchange) this will significantly alter the

geography of accessibility for the area. It will provide a more direct route to BYU from I‐15,

inducing drivers to divert from other routes. Provo 820 North becomes Provo 800 North, which

is the southern boundary of BYU. It will be the most direct route for automobile traffic bound

for both BYU and Utah Valley Hospital.

Project #20 is planned to widen Provo Center Street from Geneva Road to Provo 1600 West. It

is not expected to significantly affect traffic volumes along the diversion corridors.

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9.4 Student Enrollment & Employment

This section contains information about student enrollment and employment totals. It includes

the sources of data, and the specific metrics used to measure enrollment and employment. It

presents data for the 2011—2015 period.

9.4.1 Methods&Data

There are several different metrics used to track enrollment. Full‐time students are enrolled in

a course of study that will result in graduation in four years. The “headcount” metric is the total

count of part‐time and full‐time students. “Full‐time equivalent” is an estimate of enrollment

levels if the fractions of part‐time students were combined to be full‐time students. Enrollment

totals throughout the year are not uniform. There is substantial attrition in student attendance

over the course of year. The highest enrollment is in fall, then winter, then spring, then

summer. The counts of students for the fall semester have been used, as representative of peak

transportation demand. UVU enrollment data track enrollment by semester and were available

from their metrics dashboard website.120 BYU enrollment data was drawn from a number of

sources. Data from 2000‐2014 is the count of all‐day enrollment.121 Data for 2015 was drawn

from the local news.122

Employment is categorized by full‐time and part‐time employment. Secondary sources (such as

the UVU Factbooks123) did not provide consistently categorized totals. Instead, the data was

obtained from Human Resource Services at BYU,124 and Institutional Research & Information at

UVU.125 These same sources also provided information on the amount of overlap between

students and staff, suggesting an overlap of about 1,200 a year for BYU and an overlap of about

500 for UVU.

120 Utah Valley University Institutional Research & Information. (n.d.). Retrieved May 20, 2017, from http://www.uvu.edu/iri/enrollment/student.html 121 Ashley Urquhart, Compensation Assistant, BYU. Retrieved: Jan 27, 2017 122 Johnson, S. (2015, September 16). BYU sees 17 percent increase in returned missionaries on campus. The Daily Herald. Retrieved May 20, 2017, from http://www.heraldextra.com/news/local/education/college/byu/byu‐sees‐percent‐increase‐in‐returned‐missionaries‐on‐campus/article_b1b69750‐7522‐5181‐9bb3‐7a3ce5b1328c.html 123 Utah Valley University Institutional Research & Information. (n.d.). Retrieved May 20, 2017, from http://www.uvu.edu/iri/additionalresources/annualreports.html 124 Ashley Urquhart, Compensation Assistant, BYU. Retrieved: Jan 27, 2017 125 Geoff Matthews, Associate Director – Institutional Research & Information, Utah Valley University

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A graph of student enrollment is shown in Figure 9‐13 for UVU and Figure 9‐14 for BYU. A table

with full tabulation is shown in Figure 9‐15. Headcount refers to a count of all students; FTE

refers to an aggregation of the total credit hours from all students to the equivalent load for

full‐time students.

Figure 9‐13: Student Headcount, FTE, and Full‐Time Students for UVU

Figure 9‐14: Student Headcount, FTE, and Full‐Time Students for BYU

Total student enrollment throughout the study period is largely flat for BYU. Total student

enrollment at BYU has been capped for years. The decline in enrollment in both 2013 and 2014

at BYU can be explained by a change in LDS policies regarding eligible ages of young adults

serving an LDS religious mission. The ages of eligibility lowered was from 19 to 18 for males and

from 21 to 19 for females. Historically, many young adults would take a year of university

classes before leaving on a mission, but this change in policy indicates that many would‐be

students are leaving immediately after graduating high school. While the effect may last several

years, due to the cohort effect, it should vanish over time.

‐

10,000

20,000

30,000

40,000

'00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15

UVU Headcount UVU Full Time Equivalent UVU Full Time

25,000

27,000

29,000

31,000

33,000

35,000

'00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15

BYU Headcount BYU Full Time Equivalent BYU Full Time

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UVU was also affected by this change in policy, but to a lesser degree. However, in contrast,

UVU can be expected to see substantial increase in students in future years. In July, 2008, UVU

converted from a State College (Utah Valley State College) to a full University. While it has

experienced regular increases in enrollment prior to its conversion, enrollment increased

significantly between 2007 and 2010. Further, UVU still has both the desire and land to

substantially grow and develop its campus.

Figure 9‐15: Student Headcount, Full‐Time Equivalent, and Full‐Time Students for BYU & UVU

A graph of employment for both UVU and BYU, divided by part‐time and full‐time, is shown in

Figure 9‐16. Tabular counts of employment by full and part time for BYU are shown in Figure

9‐17. Tabular counts for UVU, for full and part time (as well as additional categories) is shown in

Figure 9‐18.

Headcount Full Time Equivalent Full Time Headcount Full Time Equivalent Full Time

'00 32,554 31,726 30,069 20,946 13,504 10,892

'01 32,771 31,925 30,234 22,609 15,163 11,757

'02 32,408 31,537 29,796 23,609 16,261 12,277

'03 33,008 32,129 30,372 23,803 16,312 12,378

'04 33,427 32,496 30,633 24,149 16,339 12,557

'05 32,920 32,111 30,492 24,487 16,081 12,733

'06 32,679 31,816 30,090 23,305 15,662 12,119

'07 32,964 32,118 30,426 23,840 16,135 12,397

'08 32,992 32,161 30,500 26,696 17,910 13,882

'09 32,955 32,148 30,533 28,765 19,670 14,958

'10 32,947 32,151 30,558 32,670 21,825 16,988

'11 32,980 32,166 30,539 33,395 22,448 17,200

'12 33,336 32,495 30,814 31,562 21,616 16,745

'13 30,243 29,417 27,766 30,564 20,697 15,755

'14 29,672 28,854 27,217 31,332 21,335 16,296

'15 32,615 31,781 30,114 33,211 22,592 17,214

Year

BYU UVU

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Figure 9‐16: Full‐Time and Part‐Time Employment for UVU and BYU 2010‐2015

Figure 9‐17: Full‐Time and Part‐Time Employment for BYU

Category 2010 2011 2012 2013 2014 2015

BYU Full Time Staff/Admin/Faculty 3,969 4,011 3,995 4,057 4,072 4,183

BYU Part Time Staff/Faculty 1,656 1,730 1,752 1,811 1,855 1,928

Total Employees 5,625 5,741 5,747 5,868 5,927 6,111

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Figure 9‐18: Full‐Time and Part‐Time Employment for UVU


Data does not track students who are part of online‐only or night school programs. The

difference between full‐time and part‐time students in terms of trip chains is unknown ‐ some

part‐time students only make the trip every other day, while other part‐time students may

make multiple trips in one day.

9.5 Land Development

This section provides detailed information about land use change within the study area.

Detecting land development relies on comparing parcels before and after the Provo‐Orem TRIP.

This is accomplished by using data from the county assessor and satellite imagery to compare

changes in land uses. Census geography also aids in detecting differences.

9.5.1 Method&Data

The assessor's data includes a GIS shapefile containing the bounds of all parcels in the county as

polygons, and a table of attributes describing aspects of each polygon. After selecting and

excluding public right‐of‐way polygons from the data set, 14,236 polygons remained. Of these

polygons, 8,345 had attribute data about the year buildings on each parcel were built. All

parcels in the assessor’s records also contain a unique ID number, variously noted as SERNO or

SERIAL. Based on that key, parcels from county assessor’s data from before the Provo‐Orem

BRT can be matched to the same parcels in a dataset after operations begin. For new

Employee Classification 2010 2011 2012 2013 2014 2015

Full Time

Executives 29 31 34 34 32 33

Exempt Salaried Staff 553 585 601 601 616 681

Faculty 519 553 582 576 590 642

Nonexempt Salaried Staff 400 416 447 462 483 502

Early Retiree 28 26 22 20 14 13

Total Full‐Time 1,529 1,611 1,686 1,693 1,735 1,871

Part Time

Adjunct/Overload Teaching 989 1,058 996 979 978 840

Part‐Time Staff 746 689 714 726 808 793

Student 950 1,033 1,041 1,028 1,192 1,235

Work Study Student 254 273 175 130 133 181

Stipend or Temporary Agreement 5 7 4 2 ‐ 6

Public Service Instructors 38 29 ‐ 56 36 218

Total Hourly 2,982 3,089 2,930 2,921 3,147 3,273

Total Employees 4,511 4,700 4,616 4,614 4,882 5,144

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development, changes in the Year Built attribute field should detect new development. For

rehabilitation, changes in the Improvement Value attribute field should detect changes.

Changes in development can also be detected by comparing aerial/satellite images. Aerial

images from 2011 were compared to aerial images from 2014. Overlaying the images in

ArcMap at 50 percent transparency shows where the two aerial images contradict one another,

making it simple to spot differences, such as the appearance of a new classroom building along

College Drive at UVU. A shapefile of building footprints from 2011 aided in the comparison.

The use of census geography to select parcels provides a control total. American Factfinder126

releases data only for the tract level, but the TIGER/Line data releases selected demographic

and economic data at the block level.127 Therefore, it is possible to compare Decennial Census

block population with the five‐year American Community Survey (ACS) estimate. For the year

2010, counts from blocks were aggregated to the block group level. From the ACS, TIGER/Line

data was used with the field “B01003e1” used for population and “B25001e1” used for housing.


Housing and population characteristics for the study area are shown in Figure 9‐19. A

substantial amount of population resides in group quarters, such as dorms. The area is the area

of census block groups, therefore the housing density and population density represent gross

density in units‐per‐acre.

Figure 9‐19: Study Area Population & Housing Units per Acre


A new version of the parcel file database is available on an annual basis. Not all data within the

database are updated on an annual basis. A typical assessment cycle is three years. Comparison

of certain fields, such as Year Built and Improvement Value, should make it possible to

recognize most land use changes.

126 United States Census Bureau. (n.d.). American Fact finder. Retrieved May 20, 2017, from http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml 127 United States Census Bureau. (n.d.). TIGER/Line. Retrieved May 20, 2017, from https://www.census.gov/geo/maps‐data/data/tiger‐data.html

Survey Housing Units Population Housing

Density

Population

Density Acres

2010 Decennial Census 19,684 70,989 2.84 10.24 6,935

2014 American Community Survey 19,330 67,761 2.79 9.77 6,936

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A large number of records lack data. The GIS shapefile of the study area contained about 14,600

parcel polygons. After manually removing public right‐of‐way polygons from the data set, about

14,000 polygons remained. Of these polygons, 8,345 had Year Built data. Many are sliver

polygons, so database cleaning and data creation was necessary. Detecting some changes

required inspection of aerial images, but aerial images were not available for all years. Aerial

images from 2011 can be compared to aerial images from 2014, with another vintage likely to

be available from state sources in 2017. For the years 2018 and 2019, recourse to Google Earth

and ESRI aerial images is likely be required.

The only timely form of census data is the American Community Survey, which uses a limited

sample. Data are only released at the county level on an annual basis. Annual updates for

smaller geographic units (tracts, block groups, blocks) rely on combining multiple years of

sampling. Thus, for each year, only one fifth of the data is a new sample. American Factfinder128

releases data only for the tract level, but the TIGER/Line data releases selected demographic

and economic data at the block level.129 They are available in geodatabase format for 2013,

2014, and 2015,130 and so can reasonably be expected to be available for future years, albeit on

an estimated two‐year time lag (2015 data in 2017, 2017 data in 2019, etc.).

128 United States Census Bureau. (n.d.). American Fact finder. Retrieved May 20, 2017, from http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml 129 United States Census Bureau. (n.d.). TIGER/Line. Retrieved May 20, 2017, from https://www.census.gov/geo/maps‐data/data/tiger‐data.html 130 United States Census Bureau. (n.d.). TIGER Geodatabases. Retrieved May 20, 2017, from https://www.census.gov/geo/maps‐data/data/tiger‐geodatabases.html

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9.6 Parking Supply & Occupancy

This section discusses parking conditions. The data and methods sub‐section provides maps of

parking locations for each university. It also details pricing and regulation. The results and

discussion sub‐section provides information about the count of stalls, the count of occupied

stalls, and occupied stalls by lot type.

9.6.1 Methods&Data

For each campus, all known parking lots were located and traced in GIS as polygons. All

automobile stalls were dotted for all parking lots. Additional points were included to represent

parking garages. Figure 9‐20 shows an example of how the stalls were dotted.

Figure 9‐20: Example of ‘Dotted’ Parking Stalls

A copy of the file was made, and all points representing vacant spaces were deleted. This

process was then repeated for different aerial images. Figure 9‐21 shows the type and location

of parking lots at UVU, including both the lots on the main campus and the lots west of I‐15.

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Figure 9‐21: UVU Parking Map

Parking Lots at UVU are divided into five classes, with four classes of parking passes. Green lots

are reserved for employees and are provided free of charge. Yellow lots are $90 a year or $60 a

semester (as are disabled lots). The parking garage is $750 a year. The purple lot, the most

distant, is a free lot.

As of fall 2016, parking lots at BYU are divided into nine classes: Student, Graduate Student,

Faculty/Staff, Restricted Visitor, Visitor, Timed, Motorcycle, Student Housing, and Free.

Motorcycle parking has been disregarded for the purpose of this study. Prior to 2015, all

parking was free on the BYU campus. Since 2015, the student and graduate lots require a pass,

costing $60 per semester. Three outlying lots remain free. All student parking lots at BYU are

outside the Campus Drive orbital ring road, and (barring two lots at 600 East and University

Parkway) all student lots are on the periphery of the BYU campus, so that access to the core of

campus requires between 1,600 and 3,200 feet of walking. The free lots are about 2,500 feet,

4,000 feet, and 4,600 feet from the center of campus. Parking is also free during spring and

summer terms. The parking map for BYU is shown in Figure 9‐22.

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Figure 9‐22: Map of Parking at BYU, 2015

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9.6.1.1 Data

Data on parking supply and availability were derived from aerial images. Four different sources

of aerial imagery data were used:

2016 National Agricultural Imagery Program 1 Meter Orthophotography

Google Statewide Aerial Photography (~6” pixels)

Microsoft Bing Maps Imagery Layer for ArcMap, hosted by ArcGIS Online

ArcMap Basemap ‘World Imagery’, hosted by ArcGIS Online

All of these sets of aerial images have different flaws and different virtues; this is typically

trade‐off between recentness and resolution.

Data from ArcMap World Image Data was initially used. While typically high resolution (6” or 1’

pixels) it is quite dated. Construction for BYU’s Campus Unification Project (2013‐2016) is not

shown on the map. According to the meta‐data for the Arcmap World Image Data131:

World Imagery provides one meter or better satellite and aerial imagery in many parts of

the world and lower resolution satellite imagery worldwide…..The map features 0.3m

resolution imagery in the continental United States and 0.6m resolution imagery in parts

of Western Europe from Digital Globe. Recent 1m USDA NAIP imagery is available in

select states of the US.

Data from Bing Maps (also hosted by ArcGIS Online) was used to validate this map. While not

data is provided, the state of construction of the BYU Heritage Halls suggests mid‐2012.

Most image service provide a mosaic of images, with different time‐stamps at different

resolution, with limited meta‐data. The NAIP data is a collected for a specific time and day, but

at lower resolution that the other image services (1 meter vs 1’ or 6”).

National Agricultural Imagery Program is color aerial photography typically collected

ever 2 or 3 years in the middle of the summer. The resolution is usually 1 meter with a

horizontal positional accuracy of 5 meters.132

The NAIP aerial imagery for the western portion of UVU was obtained August 19, 2016 at about

11:27; for the eastern portion, August 8, 2016 11:49 was the datestamp. For BYU, the imagery

131 Environmental Systems Research Institute (2017). World Imagery. Retrieved June 4, 2017, from http://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9 132 Utah AGRC [Automated Geographic Reference Center]. (n.d.). Google Imagery Information. Retrieved June 4, 2017, from https://gis.utah.gov/data/aerial‐photography/ 132 Utah AGRC [Automated Geographic Reference Center]. (

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is from August 8, 2016 at 11:29. For UVU, this data falls just before the start of Fall Semester

(August 22). For BYU, it falls just before the end of summer term (August 11th). While most

current, 1m resolution is not always sufficient to differentiate a car from a small shed.

The Google Imagery is much higher resolution than the NAIP imagery. With 6” pixels rather

than 1 meter pixels, pixel density is effectively 36 times higher. However, it is a mosaic, with no

meta‐data documenting year, day or date or the imagery. Like the NAIP imagery, the Google

License was hosted by the AGRC133.

AGRC owns a public sector license to Google's statewide 6" aerial photography. Contractors and partners may also qualify. The Google license is for color aerial photography, typically collected within 3 years, from the spring, summer or fall. The imagery is statewide. The resolution is 6 inch or better with a horizontal positional accuracy to achieve or exceed one meter (C90) in most areas without significant vertical relief. 134

The dynamic layer file at the AGRC was used to count occupied parking stalls. The AGRC

maintains and has made available an archived annual snapshot of the dynamic layer. For future

counts, using the snapshot layer is recommended, as the dynamic layer changes over time, due

to updates.

The parking garages at BYU are typically 100% full during the peak hours135. The same was

assumed for UVU.


2016 National Agricultural Imagery Program 1 Meter Orthophotography

Google Statewide Aerial Photography (~6” pixels)

Microsoft Bing Maps Imagery Layer for ArcMap, hosted by ArcGIS Online

ArcMap Basemap ‘World Imagery’, hosted by ArcGIS Online

A summary of parking stalls available and occupancy is shown in Figure 9‐23. The count

includes all stalls at any location for either university, and for all types of parking stalls

(including those reserved for police or University Administration).

133 Utah AGRC [Automated Geographic Reference Center]. (n.d.). Google Imagery Information. Retrieved June 4, 2017, from https://gis.utah.gov/data/aerial‐photography/ 134 Utah AGRC [Automated Geographic Reference Center]. (n.d.). Google Imagery Information. Retrieved June 4, 2017, from https://gis.utah.gov/data/aerial‐photography/ 135 Bob Ross, BYU Facility Services, by email June 6, 2017.

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Figure 9‐23: Parking Supply And Occupancy

The four different aerial images yield different counts of stalls, reflecting differences in the

vintage of aerial images. The same is true of occupied stalls, reflecting different conditions. ESRI

and BING counts are generally very similar. Counts based on Google Earth are generally the

highest.

BYU has a significant housing program, with university owned housing consisting of the

Helaman Halls, the Heritage Halls, Wyview, and Wymount Terrace. Helaman Halls has capacity

for about 2,100 students.136 Heritage Halls have a capacity of about 2,400 students. Wymount

Terrace has about 900 two and three bedroom units. Wyview was estimated to have about

1,000 beds. Both the Helaman and Heritage Halls are within a walkable distance (about 2,000

feet and 1,600 feet respectively) from the center of campus (the Harold B. Lee Library). The

other BYU residence halls are more distant. They are a longer than comfortable walk (Wyview

(average of 5,600 feet) and Wymount Terrace (average of 4,100 feet), and so many students

drive. None of the residential parking is available for commuting students. UVU has no on‐

campus housing.

Evidence suggests that both campuses have an abundant supply of parking. There are more

than 40 lots to park in at UVU, and more than 100 at BYU of varying sizes. Patterns in parking

occupancy suggest that students make efficient use of the parking stalls nearest to their

destinations. The most central lots are filled first, then the nearest stalls in the outlying lots, and

finally the far stalls in the outlying lots. This pattern (at UVU) is demonstrated in Figure 9‐24.

136 Omics International (n.d.). List of Brigham Young University Residence Halls. Retrieved from: http://research.omicsgroup.org/index.php/List_of_Brigham_Young_University_residence_halls. May 25, 2017.

Type NAIP Google ESRI BING NAIP Google ESRI BING NAIP Google ESRI BING

BYU 17,213 16,583 17,785 18,012 7,488 8,203 6,185 6,037 44% 49% 35% 34%

Faculty/Staff 5,735 5307 6,112 5,955 3,693 3714 3,642 3,445 64% 70% 60% 58%

Student C 242 216 244 248 33 38 6 3 14% 18% 2% 1%

Field 577 617 649 670 75 160 41 47 13% 26% 6% 7%

Grad 751 1067 786 827 549 702 490 492 73% 66% 62% 59%

Housing 2,431 2444 2,501 2,775 803 1114 592 666 33% 46% 24% 24%

Police 35 36 36 38 27 28 26 26 77% 78% 72% 68%

Timed 123 92 123 137 47 73 35 21 38% 79% 28% 15%

Visitor 478 139 444 462 144 118 240 238 30% 85% 54% 52%

Student (Y) 6,841 6665 6,890 6,900 2,117 2256 1,113 1,099 31% 34% 16% 16%

UVU 7,896 8,306 7,405 7,509 1,770 3,824 2,152 2,067 22% 46% 29% 28%

Garage 458 458 ‐ ‐ 458 458 ‐ ‐ 100% 100% 0% 0%

Green 1,618 1964 1,687 1,568 764 956 1,018 841 47% 49% 60% 54%

Purple 1,344 697 1,343 695 50 30 104 14 4% 4% 8% 2%

Visitor 147 343 157 352 51 124 137 190 35% 36% 87% 54%

Yellow 4,329 4844 4,218 4,894 447 2,256 893 1,022 10% 47% 21% 21%

Stalls OccupiedUniversity % Occupied

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Figure 9‐24: Parking Occupancy Pattern

Both Universities use a parking permit system that allows unlimited parking use, rather than a

pay‐for‐service model that charges on an hourly basis. To some extent, this is a necessity due

the large number of dispersed lots, which would otherwise require metering or a traffic gate

and parking booth. Permit systems have two limitations: they provide no incentive for students

to limit use of parking on a daily basis, and they provide an incentive to spend time searching

for the best possible spot in an array of possibilities. At BYU, there are almost 60 locations

available using an “A” parking permit. Searching for parking may be generating substantial

congestion from vehicles circling for parking. Research suggests that in business districts, this

search may represent as much as 20 percent of total traffic volume.137

137 Shoup, D. C. (2005). The high cost of free parking (Vol. 206). Chicago: Planners Press.

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Counts were estimated from aerial images. Historically, the state of Utah has obtained aerial

images every few years. However, for 2015 and later years, imagery is being obtained through a

contract with Google Imagery.138 Unlike a single aerial image, Google Imagery is a mosaic of

images, with different scales displaying imagery from different dates and years. Hence, it is

impossible have photos taken at consistent times of day and year for all parking lots. Using

aerial imagery to count occupied stalls may understate the occupancy rate due to variation in

time of day, and time of year. Tree cover may also bias the count.

Aerial images are not real‐time. Historically, Google Imagery is between one and three years

old.139 For a growing urban area, the imagery is likely updated more frequently than average, so

an updated aerial photograph is estimated to be available every other year. Typically, the

newer imagery is higher‐resolution photography, and the closest level of zoom should

represent the most recent data. Correspondingly, different levels of zoom represent different

time periods.

There is substantial construction at both campuses. UVU has added new buildings and new

parking lots in the past year. At BYU, the construction of new housing, new buildings, and the

“Universe” project (which included the closure of part of Campus Drive) have caused

substantial changes in parking availability on campus. Finally, since the parking count, BYU has

begun charging for parking, so only the most peripheral lots remain free. This will likely have an

effect on parking behavior.

Ideally, to be most accurate, future counts should be done on‐site, during fall semester, on the

same day of the week, and at a consistent time of the day. To do so would require counting

about 24,000 parking stalls in over 150 occupied lots within a few hours on the same day. Due

to the large number of stalls and lots, this was felt not to be feasible.

138 Utah AGRC [Automated Geographic Reference Center]. (n.d.). Google Imagery Information. Retrieved May 20, 2017, from https://gis.utah.gov/data/googleimagery/ 139 Google Maps. (n.d.). See 3D Images with Earth View. Retrieved May 20, 2017, from https://support.google.com/maps/answer/2789536?hl=en

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9.7 Crash Rates within Study Area

9.7.1 Methods&Data

Data on crashes was obtained from UDOT’s open data repository then mapped and symbolized

for diversion corridors.140 The Crash Rate Score (2011—2013) was used as the most applicable

measure. The Crash Rate Score was estimated using the following methodology.

The Crash Rate Score indicates which road segments have the highest crash rate when

compared to the statewide average crash rate for roadways of similar functional class and

traffic volume. The crash rate is calculated from the most recent three years of data (2011—

2013), while statewide average crash rates reflect five years of data (2009—2013). Crash Rate

Scores are reported on a 0 to 5 scale, with 5 representing the group of road segments with the

highest ratio of actual crash rate vs statewide average crash rate (weighted by roadway center

line miles).141

Secondly, all crashes in Utah County were geocoded and then assigned to the nearest road

segment to provide a count of crashes. Using the AADT and length of each segment, a Crash

Rate Score was calculated for all roadway segments. The data was provided by Clancy Black at

UDOT, in May 2016, for 2010—2015. The average crashes displayed are five‐year averages

(2011—2015).


The map of Crash Rate Score is presented in Figure 9‐25.The map shows which project

segments are disproportionately dangerous compared to roads with similar functional classes

and AADTs throughout Utah.

The section of University Parkway intersecting I‐15 has the highest crash rate on BRT alignment.

Starting in 2012, the area has been under construction, including signal work at the

interchange, ramp reconstruction, and the addition of a continuous flow intersection at the

intersection with Sandhill Drive, south of UVU.142

140 Utah Department of Transportation. (n.d.) Crash Rate Score. Retrieved July 6, 2016 from: http://udot.uplan.opendata.arcgis.com/datasets?q=Crashes 141 Utah Department of Transportation (2016). Open Data Guide. Retrieved July 6, 2016 from:

http://www.arcgis.com/home/item.html?id=e2f0216b81b14fe0b41bfe628b6dbf9e

142 Davidson, L. (2012, April 13). I‐15 rebuild shifts focus to interchange work. Salt Lake Tribune. Retrieved May 20, 2017, from http://archive.sltrib.com/story.php?ref=/sltrib/politics/53901609‐90/cfi‐close‐construction‐interchange.html.csp

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Figure 9‐25: Map of Alignment and Diversion Corridor Crash Rates

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The intersection of University Parkway with University Avenue and the intersection of

University Avenue with 800 North in Provo are also likely crash hotspots. Two segments of the

alignment have a high Crash Rate Score: University Parkway between Orem Main Street and

Orem 800 East and University Avenue south of 800 North in Provo. The first is a major

commercial corridor with a large number of access points, and the second passes through the

Provo central business district (CBD). Both sections are slated for exclusive BRT lanes, replacing

median turn lanes. Exclusive lanes typically involve a separation barrier, which eliminates a

driver’s ability to make dangerous left turns across traffic. This, in turn, should improve safety.

In Provo’s CBD, intersection density is high enough that the street grid could satisfy the demand

for left turns at other intersections. Along University Parkway, alternate means of satisfying the

demand for left turns should be explored.

The count of actual crashes and the calculated Crash Rate Score for all roads in the project area,

along with their expected Crash Rate Score based on their VMT and functional class, can be

found in APPENDIX C. Crash Locations.

9.7.3 Data&MethodologyLimitations

Crash Rate Scores are based on analysis performed for 2009—2013. No update is currently

pending. If UDOT does not update the Crash Score Rating, doing so independently will be

necessary. Given the simplicity of the methodology, this is feasible. Doing so would require

obtaining data on crashes at a statewide level, which is already collected. The Utah Motor

Vehicle Crash Report is produced annually, as part of an ongoing reporting scheme mandated

by both state and federal governments. It began decades ago, and seems likely to continue into

the foreseeable future. The data has latitude‐longitude codes, but will need to be geocoded

and then assigned to the relevant road. Only with an updated Crash Rate Score will it be

possible to detect these types of changes on roads in the study area.

Once this data has been collected, it will become possible to map the results, with one map for

expected crash rates, one map with the actual average crashes per year, and one map with the

deviance between them, including a metric to gauge the differences.

With a calculated crash rate for all study years, additional and more rigorous analyses would be

possible. It would be possible to conduct an analysis of variance (ANOVA) on the data inputs to

the Crash Rate Score. A step beyond that would be a negative binomial model of the crashes,

based on a variety of roadway, traffic, and weather conditions, which would have substantially

greater predictive capacity.

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10 Summary The Provo‐Orem BRT before and after study uses a quasi‐experimental design with a pre‐test

and post‐test to determine the effect on traffic, development, safety, and air quality of the

opening of the Provo‐Orem Bus Rapid Transit Project. This type of research design is known as a

natural experiment. The intent of the Initial Conditions Report is to provide a complete and

comprehensive account of the initial conditions along the BRT alignment. This will make it

possible to compare conditions before and after the introduction of BRT service.

The specific objectives are to measure changes in:

Automobile traffic in the corridor between baseline and forecast conditions;

Land uses along the corridor;

Parking occupancy at the two universities;

Transit ridership; and

Other co‐benefits of reduced automobile traffic, such as reduced vehicle emissions and

reduction in the number and severity of traffic collisions.

Automobile traffic counts from UDOT AADT were validated with Automatic Traffic Signal

Performance Measure counts. The initial conditions suggest that automobile traffic is flat on

many corridors, but that total traffic on all parallel corridors, as measured by cut‐lines, is rising.

The land use type of all parcels in the study area were identified. Each was assigned an ITE land

use code. Trip generation was estimated using ITE trip‐generation data. The number of parking

stalls available at each university was measured using multiple aerial imagery counts. A

procedure to estimate vehicle emissions has been devised using Federal Air Quality

measurement models (GREET and MOVES) and were used to estimate the volume of current

emissions by type. Crashes are associated with road segments using latitude‐longitude point

data of accident locations.

This is the first report in a series, the “Before”’ report. In the years following the start of BRT

operations, a series of “After” reports will be written. They will compare the conditions before

and after the BRT began to affect transportation and land use conditions. One of the key

elements of this report is documenting confounding or potentially confounding factors that

could affect the later analyses. Subsequent reports will control for potentially confounding

factors and offer conclusions on the changes in conditions caused by the Provo‐Orem BRT

project.

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PROVO-OREM TRANSPORTATION IMPROVEMENT PROJECT (TRIP) APPENDICES

Prepared for:

Utah Department of Transportation

Research Division

Submitted by:

University of Utah,

Metropolitan Research Center

Authored by:

Matthew M. Miller, Mercedes Beaudoin,

and Reid Ewing

Final Report -

June 2017

Report No. UT‐17.XX

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11 APPENDIX A. LITERATURE REVIEW 11.1 Introduction

The use of arterial Bus Rapid Transit (BRT) and BRT‐type systems is a relatively recent

development in the United States, although there are several systems currently in operation

and many more in the planning process (Perk et al. 2010). The literature on BRT is vast, yet a

majority it focuses on operational performance, project implementation, or economic

development components. Unfortunately, this means there is a large gap in the BRT literature

regarding its effects on traffic safety, capacity, and congestion. Thus, this literature review uses

peer‐reviewed articles, technical reports, and U.S. BRT case studies to provide an overview of

the impacts of arterial BRT on safety, congestion, and traffic volumes. A majority of these BRT

peer‐reviewed studies are conducted in Latin American and other developing countries. This

review focuses on studies in North America.

Several reports in the literature have stated that BRT provides benefits such as reduced crashes,

illness, crime, and environmental pollutants that effect human health (Wright & Fjellstrom, 2003).

Nevertheless, the topic of BRT traffic safety is nascent and undeveloped compared to

traditional BRT literature focused on operational performance and project implementations (Hidalgo & Carrigan 2010); (CERTU 2009); (Levinson, Zimmerman, Clinger, & Gast, 2003).

11.2 Bus Rapid Transit and Safety

This section provides an overview of BRT crash types, a review of existent literature attempting

to measure BRT’s effect on safety, and a brief description of the various design

recommendations for BRT safety. The effects of BRT systems improving road safety remain

unclear, as very few empirical studies test this and those that do provide mixed evidence (Vecino‐Ortiz & Hyder, 2015).

11.2.1 BRT&TrafficCrashes

BRT systems have been pronounced to improve road safety for three reasons. First, BRTs

organize transportation systems, reduce their motorization, and refurbish their surrounding

infrastructure (Vecino‐Ortiz & Hyder, 2015). Second, BRTs typically separate buses from other

vehicles and pedestrians, which not only helps to prevent contact but decreases the speed in

mixed traffic (Vecino‐Ortiz & Hyder, 2015). Finally, BRTs improve fleet quality and typically provide

transport drivers training (Cordeiro, Schipper, & Noriega, 2006; Vecino‐Ortiz & Hyder, 2015) Still, as

arterial BRTs are not entirely separated from general traffic, crashes do occur (Nikitas & Karlsson,

2015). Thee six most common BRT crash types (Duduta, Adriazola, Hidalgo, Lindau, & Jaffe, 2015) are

identified in Figure 11‐1

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Figure 11‐1: Common BRT Crashes

Type of Crash Description

Pedestrians in the bus lane

Pedestrians may cross through slow or stalled mixed traffic only to be struck by a bus travelling on a dedicated bus lane. Bus drivers also have little time to react as their view of pedestrians crossing through traffic is often obstructed by the vehicles on the road. This type of crash usually results in fatal injuries.

Left turns across a bus lane

This is one of the most common types of collision between buses and general traffic when median bus lanes are used. If left turns at intersections are not restricted or controlled, a vehicle when making a left turn cuts across the bus lane and can be struck by a bus going straight through the intersection.

General traffic in bus lanes

This is a common crash type when dedicated bus lanes are provided. The lack of a physical barrier between bus lanes and general traffic lanes can allow other vehicles to illegally enter the bus lanes and collide with buses.

Crashes between buses and cyclists

Cyclists sometimes use dedicated bus lanes, because they perceive them to be safer than mixed traffic lanes but can face serious injury when hit by fast moving buses. Cyclists sometimes also attempt evasive maneuvers into other lanes when buses approach, which may cause them to be hit by a vehicle from the opposite direction or lose control and hit the dividers. At curbside bus stops, buses merging into mixed traffic may potentially be dangerous to cyclists.

Rear‐end collisions at a bus stop or station

This occurs when a bus is lining up behind another bus at a station platform but is coming in too fast and collides with the bus in front.

Crashes between buses at stations

These crashes occur on multi‐lane busways with express lanes. Buses leaving the station and merging onto the express lane collide with buses in the express lane, either traveling through or attempting to access the station. A collision with an express bus is more severe as they travel at higher speeds.

11.2.2 BRTLiteratureAttemptingtoMeasureSafety

While there have been claims that BRT systems improve road safety, the evidence of these

claims has not been scientifically assessed. There is one notable exception(Vecino‐Ortiz & Hyder,

2015); they conducted a literature search using a strict protocol that includes papers whose

primary theme is BRT road safety, were peer‐reviewed, and that also presented quantitative

empirical data. Their results: out of 879 entries, only four entries met the protocol and were

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selected for evaluation: (Bocarejo et al., 2012) (Duduta, Adriazola, Hidalgo, Lindau, & Dos Santos, 2013),

(Chun Keong Goh & Logan, n.d.), and (Duduta, Adriazola, Hidalgo, Lindau, & Jaffe, 2012). Most of the

excluded literature either did not directly address the effect of BRT on road safety and/or did

not provide data to empirically support such effects.

Issues identified with these studies(Vecino‐Ortiz & Hyder, 2015):

(1) the heterogeneity of BRT systems’ surrounding environments is seldom considered and

therefore challenging to compare different BRT systems’ effects;

(2) many cities which implement BRT concurrently invest in other road safety infrastructure

and policies which implies unobserved actions may also affect reduced death rates;

(3) many studies lack appropriate tools to infer causality with observational and non‐

experimental data, especially using before‐and‐after studies without bona fide

counterfactuals; and

(4) instead of empirical data, some BRT support is founded on modeling infrastructure

changes which implies an urgent need for real world evaluations.

While this research supports Vecino‐Ortize and Hyder’s (2015) conclusions, the authors want to

identify another issue: no known comprehensive before‐and‐after studies have been conducted

on the safety effects of arterial BRT systems in the U.S. context.

After conducting our own literature review of peer‐reviewed articles and system‐specific

technical reports, we found no U.S.‐oriented BRT systems analyzed for safety effects. In fact,

few documents identify important safety measures (e.g., the number of crashes that occurred

on the corridor prior to or after the BRT intervention. Figure 11‐2 provides an overview of the

findings from these system‐specific reports.

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Figure 11‐2: Vecino‐Ortize and Hyder's review of BRT safety studies

11.2.3 BRTDesignandSafety

BRT systems, if designed appropriately, can curtail adverse traffic and pedestrian impacts

(Levinson et al., 2003); (Santos‐Reyes & Ávalos‐Bravo, 2014).While some claim or repeat others’ claims

that a BRT system improves safety (Bocarejo et al., 2012); (Cervero, 2013);(Duduta et al., 2012); (Duduta et al., 2013); (Echevvery et al 2005); (Chun Keong Goh & Logan, n.d.); (Hidalgo & Yepes, 2005); (Hidalgo et al., 2012);

(Singhal, Kamga, & Yazici, 2014) (Yazıcı, Ilıcalı, Camkesen, & Kamga, 2013) from a review (Vecino‐Ortiz & Hyder,

2015) it is apparent that there is no systemic evidence of these claims. Even with their analysis,

this section is used to briefly review the BRT system design elements that are considered to

influence traffic, and pedestrian safety.

Authors and

DateMethod/Model

BRT System

/LocationFindings

Vecino‐Ortize and Hyder’s (2015)

critique/comment

Bocarejo et

al., 2012

Before/after analysis of the road

safety implications between 1998

and 2008 using Geographic

Information Systems (GIS)

Transmilenio/Ca

racas corridor in

Bogota,

Colombia

Mixed: The BRT was correlated to an

overall reduction (60%) in traffic crashes

along its corridor, yet crashes increased

around some areas seemingly related to

higher speeds in mixed traffic and more

pedestrians around stations.

Given a before/after analysis was used with

multiple confounders, in addition to the mixed

findings, it cannot be concluded that the BRT

caused road safety improvements.

Duduta et al.,

2012

Mixed‐methods including a

before/after and regression count

data analyses with crash data to

evaluate the effect of BRT on road

safety including road safety

inspections and interviews with

employees in nine BRT systems. On

three of the systems, the authors

conducted difference of means

tests to estimate the changes in

road crashes before and after the

implementation of the study.

Bogota

(Colombia);

Guadalajara

(Mexico); Delhi

(India)

BRT is correlated with a reduction in

crashes, but not statistically significant.

The safety benefits occurred from

changes in street geometry (the debut

of a BRT lane) which (a) reduced the

number of legs at certain intersections,

(b) reduced the number of lanes to

accommodate stations, (c) restricted left

turns, and (d) the central median

shortened pedestrian crossings. In

Bogota, fatalities decreased by 60 %. In

Guadalajara, monthly crashes along the

corridor decreased by nearly 50 %.

However, in Delhi, traffic fatalities more

than doubled after the BRT possibly due

to increased pedestrian exposure to

buses.

The variability of findings across the cities

demonstrates there is no a “one‐fits‐all

formula” for BRT systems.

Duduta et al.,

2013

Authors created a Bayesian model

to retrospectively estimate crashes

and injuries around BRT systems.

Macrobus,

Guadalajara

(Mexico)

The BRT reduced road crashes by 56 %

over a period of three years.

One assumption of their model is that road

safety changes are a direct function of the

BRT system, therefore there was no empirical

proof that those changes would not have

happened regardless due to unobserved

factors.

Goh et al.,

2013

Mixed‐methods: an analyses of

aggregated data and a safety audit.

Melbourne,

Australia

A 14 % reduction in road crashes in the

city. The audit review also found

negative qualitative impacts of the BRT

such as more complex side street exits.

As the analyses were based on aggregated

crash data and on before/after analyses,

causal implications cannot be made as to the

effect of BRT.

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BRT System Lanes: (Duduta et al., 2015) estimate that nine percent of all crashes occur in the BRT

lanes, while the vast majority of traffic crashes occur in the general traffic lanes and do not

involve buses at all. One of the worst designed lanes one could have for a BRT is a counter‐flow

(meaning, going the opposite way as traffic) lane. Counter‐flow BRT lanes have been correlated

with higher crash rates for both vehicles and pedestrians (Duduta et al., 2015); (Duduta et al., 2013);

(Miller, 2009); (Vecino‐Ortiz & Hyder, 2015). Contrarily, center lane‐configurations for BRT systems

have repeatedly been said to reduce collisions. Then again, (Miller, 2009) simply states that any

fully grade‐separated, segregated BRT lane has highest level safety (among other

characteristics—highest cost, reliability, etc.) of any BRT running way type.

Intersections: (Duduta et al., 2015) found that size and complexity of intersections as well as road

width are the most reliable predictors of crash frequencies along BRT corridors. The number of

approaches per intersection, the number of lanes per approach, and the maximum pedestrian‐

crossing distance are some of the key factors that influence intersection safety.

Pedestrian safety: Designing a safe BRT system not only involves its running ways, but its surrounding urban context including a city’s population. Pedestrians account for a majority of traffic fatalities long BRT corridors (Duduta et al., 2012), particularly when pedestrians attempt to cross the street and are struck by vehicles. Mid‐block signalized crossings and/or traffic calming measures have made positive safety impacts in other traffic studies (Diogenes & Lindau, 2010); (Duduta et al., 2012); (Duduta et al., 2013); (Elvik and Vaa, 2004) but have not been measured for BRT operations. Still, this reflects how the design of the walking environments around BRT systems are equally as important as BRT lanes and traffic intersections. In fact, some researchers (Wright

& Fjellstrom, 2003) believe that smart design of dedicated pedestrian zones around BRTs can be mutually beneficial for both pedestrians and the BRT system. Crosswalks should reach median bus lanes, bus lanes should be positioned at signalized locations when possible, and all components should be designed to discourage errant crossings (Miller, 2009).

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11.3 Bibliography

Bocarejo, J. P., Velasquez, J. M., Díaz, C. A., Tafur, L. E., Bogotá, L., Bocarejo, J. P., … Tafur, L. E. (2012). Impact of Bus Rapid Transit Systems on Road Safety. Transportation Research Record: Journal of the Transportation Research Board, 2317, 1–7. https://doi.org/10.3141/2317‐01

Cervero, R. (2013). Bus Rapid Transit (BRT): An Efficient and Competitive Mode of Public Transport. IURD Working Paper 2013‐01, (October), 1–36. Retrieved from http://escholarship.org/uc/item/4sn2f5wc.pdf

Chun Keong Goh, K., & Logan, D. (n.d.). Investigating the Road Safety Impacts of Bus Priority Using Experimental Micro‐Simulation Modelling. TRB. Retrieved from http://docs.trb.org/prp/14‐1894.pdf

Cordeiro, M., Schipper, L., & Noriega, D. (2006). Measuring the Invisible Quantifying Emissions Reductions. Queretaro case study. World Resources Institute, (March). Retrieved from http://www.wri.org/publication/measuring‐invisible‐1

Diogenes, M., & Lindau, L. (2010). Evaluation of Pedestrian Safety at Midblock Crossings, Porto Alegre, Brazil. Transportation Research Record: Journal of the Transportation Research Board, 2193(2193), 37–43. https://doi.org/10.3141/2193‐05

Duduta, N., Adriazola, C., Hidalgo, D., Lindau, L. A., & Dos Santos, P. M. (2013). The Relationship Between Safety , Capacity , and Operating Speed on Bus Rapid Transit Case Study : Transoeste Brt , Rio De Janeiro. In CONFERENCE (pp. 1–21).

Duduta, N., Adriazola, C., Hidalgo, D., Lindau, L. A., & Jaffe, R. (2015). Traffic safety in surface public transport systems: a synthesis of research. Public Transport, 121–137. https://doi.org/10.1007/s12469‐014‐0087‐y

Duduta, N., Adriazola, C., Hidalgo, D., Lindau, L., & Jaffe, R. (2012). Understanding Road Safety Impact of High‐Performance Bus Rapid Transit and Busway Design Features. Transportation Research Record: Journal of the Transportation Research Board, 2317, 8–14. https://doi.org/10.3141/2317‐02

Levinson, H. S., Zimmerman, S., Clinger, J., & Gast, J. (2003). Bus Rapid Transit Synthesis of Case Studies. Transportation Research Record.

Miller, M. A. (2009). Bus Lanes/Bus Rapid Transit Systems on Highways: Review of the Literature. REPORT.

Nikitas, A., & Karlsson, M. (2015). A Worldwide State‐of‐the‐Art Analysis for Bus Rapid Transit: Looking for the Success Formula. Journal of Public Transportation, 18(1), 1–33. https://doi.org/10.5038/2375‐0901.18.1.3

Santos‐Reyes, J., & Ávalos‐Bravo, V. (2014). A preliminary analysis of two Bus Rapid Transit

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accidents in Mexico City. Procedia Engineering, 84, 624–633. https://doi.org/10.1016/j.proeng.2014.10.479

Singhal, A., Kamga, C., & Yazici, A. (2014). Impact of weather on urban transit ridership. Transportation Research Part A: Policy and Practice, 69, 379–391. https://doi.org/10.1016/j.tra.2014.09.008

Vecino‐Ortiz, A. I., & Hyder, A. A. (2015). Road Safety Effects of Bus Rapid Transit (BRT) Systems: a Call for Evidence. Journal of Urban Health, 92(5), 940–946. https://doi.org/10.1007/s11524‐015‐9975‐y

Wright, L., & Fjellstrom, K. (2003). Sustainable Transport: A Sourcebook for Policy‐makers in Developing Cities. Module 3a: Mass Transit Options. Retrieved from https://trid.trb.org/view.aspx?id=788294

Yazıcı, M. A., Ilıcalı, M., Camkesen, N., & Kamga, C. (2013). A Bus Rapid Transit Line Case Study : Istanbul’s Metrobus System. Journal of Public Transportation, 16(1), 153–177. https://doi.org/http://dx.doi.org/10.5038/2375‐0901.16.1.8

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12 APPENDIX B.: ITE Trip Generation Rates ITE Code

Description Per_DU PER_KSF Per_Misc Misc_Metric

22 General Aviation Airport 1.97 Flight

90 Park&Ride w/ Bus Service 4.5 Parking Space

110 General Light Industrial 6.97

120 General Heavy Industrial 1.5

130 Industrial Park 6.83

140 Manufacturing 3.82

150 Warehousing 3.56

151 Mini Warehouse 2.5

160 Data Center 0.99

170 Utilities 8

210 Single Family Homes 9.52

220 Apartment 6.65

221 Low Rise Apartment 6.59

222 High Rise Apartment 4.2

223 Mid‐Rise Apartment 4.4

230 Resd. Condo/Townhouse 5.81

240 Mobile Home Park 4.99

245 Student Housing 2.82

251 Senior Adult Housing‐Detached 3.68

252 Senior Housing‐ Attached 3.44

253 Congregate Care Facility 2.02

254 Assisted Living 2.74 Occ. Beds

260 Recreational Homes 3.16

310 Hotel 8.17 Rooms

311 All Suites Hotel 4.9 Rooms

312 Business Hotel 6.2 Occ. Room

320 Motel 5.63 Rooms

411 City Park 1.89 Acres

430 Golf Course 35.74 Holes

437 Bowling Alley 33.33 Lanes

441 Live Theater 0.2 Seats

444 Movie Theater w/ Matinee 267 Movie Screens

465 Ice Rink 1.26 Seats

495 Recreational Community Center 33.82

520 Elementary School 15.43

522 Middle/Jr. High School 13.78

530 High School 12.89

536 Private School (K‐12) Students

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ITE Code


540 Junior/ Community College 27.49

550 University/College 1.71 Students

560 Church 9.11

561 Synagogue 10.64

565 Daycare Center 74.06

580 Museum 6.6

590 Library 56.24

591 Lodge/Fraternal Organization 0.29 Members

610 Hospital 6.95

620 Nursing Home 7.6

630 Clinic 31.45

710 General Office 11.03

715 Single Tenant Office Building 11.65

731 State Motor Vehicles Department 166.02

732 US Post Office 108.19

733 Gov. Office Complex 27.92

750 Office Park 11.42

810 Tractor Supply Store 14

811 Construction Equipment Rental Store 9.9

812 Building Materials/Lumber 45.16

813 Free‐Standing Discount Superstore 50.75

815 Free‐Standing Discount Store 57.24

817 Nursery (Garden Center) 68.1

820 Shopping Center 42.7

823 Factory Outlet Center 26.59

826 Specialty Retail Center 44.32

841 Automobile Sales 32.3

842 Recreational Vehicle Sales 25.4

843 Automobile Parts Sales 61.91

848 Tire Store 24.87

850 Supermarket 102.24

853 Convenience Market w/Gas Pumps 845.6

857 Discount Club 41.8

861 Sporting Goods Superstore 18.4

862 Home Improvement Store 30.74

864 Toy/Children's Superstore 49.9

866 Pet Supply Superstore 33.8

867 Office Supply Superstore 34

872 Bed and Linen Superstore 22.2

876 Apparel Store 66.4

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ITE Code


880 Drugstore w/o Drive‐Thru 90.06

881 Drugstore w/ Drive‐Thru 96.91

887 Medical Equipment Store 6

890 Furniture Store 5.06

911 Walk‐in Bank 121.3

912 Drive‐in Bank 148.15

918 Hair Salon 19.3

920 Copy, Print, and Express Ship Store 122.7

931 Quality Restaurant 89.95

932 High Turnover/Sit Down Rest 127.15

933 Fast Food w/o Drive Thru 716

934 Fast Food w/ Drive Thru 496.12

937 Coffee/ Donut Shop w/ Drive Thru 818.58

939 Bread/ Donut/ Bagel Shop w/o Drive Thru

702.2

940 Bread/ Donut/ Bagel Shop w/ Drive Thru

386

941 Quick Lube Shop 40 Service Bays

942 Automobile Care Center 35.1

943 Automobile Parts and Service Center 44.6

944 Gasoline/Service Station 168.56 Fuel Position

945 Service Station w/ Convenience Market

162.78 Fuel Position

946 Service Station w/ Convenience Market and Carwash

152.84 Fuel Position

947 Self‐Service Carwash 108 Stalls

1000 Vacant 0 0

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13 APPENDIX C. Crash Locations

Road Functional

Class Total

Crashes

Average AADT

Actual Crash Rates

Expected Crash Rates

Difference

1700 N via Sandhill Rd ‐ SR 265 Collector 0 1,837 0 2.68 ‐2.68

Jct SR 114 via Independence Blvd.

Collector 0 1,952 0 2.68 ‐2.68

SR 189 University Avenue via 3700 North

Collector 17 7,986 1.51 2.68 ‐1.17

SR 265 via 2100 W (Main St) ‐ 800 S Orem

Collector 5 3,625 2.1 2.68 ‐0.58

1460 North via 200 W Provo/2100 W Orem ‐ 2000 S Orem

Collector 10 4,580 1.44 2.68 ‐1.24

3650 N (Quail Valley Dr) ‐ 4525 North Street via Timpview Drive

Collector 1 798 1.53 2.68 ‐1.15

2200 North viaTimpview Drive (650 East) Provo

Collector 19 5,353 2.51 2.68 ‐0.17

Jct 600 South ‐ SR 114 via 2050 West

Collector 2 2,617 1.27 2.68 ‐1.41

Jct 200 West ‐ SR 89 via 600 South in Provo

Collector 6 3,645 1.55 2.68 ‐1.13

Center Street via 400 East Orem Collector 11 6,197 3.24 2.68 0.56

800 South via 400 East Orem Collector 17 4,454 3.45 2.68 0.77

Center Street via Orem Boulevard

Collector 17 8,292 3.6 2.68 0.92

1100 W via 200 N ‐ 700 E Provo Collector 17 2,607 3.61 2.68 0.93

400 South via Orem Boulevard Collector 27 9,515 4.8 2.68 2.12

800 South via Orem Boulevard Collector 24 8,158 4.98 2.68 2.3

Jct SR 114 ‐ Carterville Road via 400 South in Orem

Collector 51 4,593 3.36 2.68 0.68

1860 South ‐ SR 189 via East Bay Boulevard

Collector 14 6,880 2.69 2.68 0.01

Grandview Lane via Columbia Lane ‐ SR 89 Orem

Collector 15 11,178 1.48 2.68 ‐1.2

SR 189 (University Ave) ‐ Freedom Blvd via 1230 N. Provo

Collector 111 17,981 6.2 2.68 3.52

450 E via Campus Dr ‐ Canyon Rd (150 E) Provo

Collector 28 16,786 3.54 2.68 0.86

700 North (Route 3030) via 700 East Provo

Collector 25 13,036 1.99 2.68 ‐0.69

300 South (SR 89) via 700 East Provo

Collector 16 4,929 3.26 2.68 0.58

4000 North Quail Valley Drive via Foothill Drive

Collector 7 3,002 1.84 2.68 ‐0.84

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Road Functional

Class Total

Crashes

Average AADT

Actual Crash Rates


Difference



Collector 0 1,952 0 2.68 ‐2.68


Collector 17 7,986 1.51 2.68 ‐1.17


Collector 5 3,625 2.1 2.68 ‐0.58


Collector 10 4,580 1.44 2.68 ‐1.24


Collector 1 798 1.53 2.68 ‐1.15


Collector 19 5,353 2.51 2.68 ‐0.17


Collector 2 2,617 1.27 2.68 ‐1.41


Collector 6 3,645 1.55 2.68 ‐1.13




Collector 17 8,292 3.6 2.68 0.92


900 East via Temple View Drive Collector 12 1,850 2.74 2.68 0.06

400 South via 400 West Orem Collector 21 7,996 2.4 2.68 ‐0.28

700 East via 700 North Provo Collector 14 5,717 4.22 2.68 1.54

800 South via 400 West Orem Collector 7 6,000 2.13 2.68 ‐0.55

SR 189 University Avenue via 700 North Provo

Collector 39 8,792 6.43 2.68 3.75

Jct Campus Drive ‐ 900 East via Heritage drive

Collector 0 1,837 0 2.68 ‐2.68

SR 265 University Parkway (BYU Diagonal) via 400 West

Collector 26 4,723 8.24 2.68 5.56

Seven Peaks Blvd. via 300 North Provo

Collector 0 1,932 0 2.68 ‐2.68

Jct 600 South ‐ 500 North via 900 West in Provo

Collector 12 2,755 3.98 2.68 1.3

Jct Campus Drive ‐ 1650 North via 450 East

Collector 1 9,650 0.45 2.68 ‐2.23

1600 West Provo at 1150 South Collector 13 1,038 9.77 2.68 7.09

Columbia Lane via 1460 North Provo

Collector 30 11,836 2.86 2.68 0.18

134 of 142


Road Functional

Class Total

Crashes

Average AADT

Actual Crash Rates


Difference



Collector 0 1,952 0 2.68 ‐2.68


Collector 17 7,986 1.51 2.68 ‐1.17


Collector 5 3,625 2.1 2.68 ‐0.58


Collector 10 4,580 1.44 2.68 ‐1.24


Collector 1 798 1.53 2.68 ‐1.15


Collector 19 5,353 2.51 2.68 ‐0.17


Collector 2 2,617 1.27 2.68 ‐1.41


Collector 6 3,645 1.55 2.68 ‐1.13




Collector 17 8,292 3.6 2.68 0.92


Main Street / 2100 West at 1460 North Provo

Collector 15 4,393 2.78 2.68 0.1

900 West ‐ 700 East via 500 North in Provo

Collector 53 9,082 3.63 2.68 0.95

900 East via Center Street Collector 7 4,380 3.24 2.68 0.56

3110 West Road to Airport ‐ Geneva Road (SR 114)

Collector 8 5,075 1.52 2.68 ‐1.16

1150 South ‐ Center Street (SR 114) Provo via 1600 West

Collector 4 3,670 0.95 2.68 ‐1.73

2050 West ‐ 900 West via 600 South in Provo

Collector 6 918 5.58 2.68 2.9

SR 189 via 1860 South Collector 23 3,316 2.45 2.68 ‐0.23

Center Street Provo at 3110 West

Collector 2 2,058 1.07 2.68 ‐1.61

Jct SR 114 ‐ Columbia Lane via 2000 South

Collector 19 2,217 4.06 2.68 1.38

SR 75 North Springville/Provo Interstate 251 98,647 1.48 1.12 0.36

SR 189 University Avenue Provo Interstate 199 90,245 0.91 1.12 ‐0.21

SR 265 BYU Diagonal/University Pkwy

Interstate 250 131,855 1.11 1.12 ‐0.01

135 of 142


Road Functional

Class Total

Crashes

Average AADT

Actual Crash Rates


Difference



Collector 0 1,952 0 2.68 ‐2.68


Collector 17 7,986 1.51 2.68 ‐1.17


Collector 5 3,625 2.1 2.68 ‐0.58


Collector 10 4,580 1.44 2.68 ‐1.24


Collector 1 798 1.53 2.68 ‐1.15


Collector 19 5,353 2.51 2.68 ‐0.17


Collector 2 2,617 1.27 2.68 ‐1.41


Collector 6 3,645 1.55 2.68 ‐1.13




Collector 17 8,292 3.6 2.68 0.92


Orem Center Street Interstate 179 129,177 1.27 1.12 0.15

SR 189 University Avenue Provo Interstate 199 90,245 0.91 1.12 ‐0.21

SR 114 Provo Center Street Interstate 448 104,198 1.14 1.12 0.02

Orem Center Street Interstate 179 129,177 1.27 1.12 0.15

SR 75 North Springville/Provo Interstate 251 98,647 1.48 1.12 0.36

SR 265 BYU Diagonal/University Pkwy

Interstate 250 131,855 1.11 1.12 ‐0.01

SR 114 Provo Center Street Interstate 448 104,198 1.14 1.12 0.02

400 West Orem via 1200 South Minor Arterial

59 5,371 7.96 2.69 5.27

Carterville Rd via 3700 N (800 S) ‐ University Ave (SR 189)

Minor Arterial

30 12,373 2.77 3.4 ‐0.63

SR 89 (State Street) via 800 South Orem

Minor Arterial

75 14,970 4.58 3.4 1.18

400 West (Route 2958) via 800 South Orem

Minor Arterial

57 8,643 7.09 2.69 4.4

800 West via 800 South Orem Minor Arterial

8 8,008 1.82 2.69 ‐0.87

1200 West (Route 2980) via 800 South Orem

Minor Arterial

8 3,940 3.71 2.69 1.02

136 of 142


Road Functional

Class Total

Crashes

Average AADT

Actual Crash Rates


Difference



Collector 0 1,952 0 2.68 ‐2.68


Collector 17 7,986 1.51 2.68 ‐1.17


Collector 5 3,625 2.1 2.68 ‐0.58


Collector 10 4,580 1.44 2.68 ‐1.24


Collector 1 798 1.53 2.68 ‐1.15


Collector 19 5,353 2.51 2.68 ‐0.17


Collector 2 2,617 1.27 2.68 ‐1.41


Collector 6 3,645 1.55 2.68 ‐1.13




Collector 17 8,292 3.6 2.68 0.92


1230 North via 200 West Provo Minor Arterial

42 10,165 4.34 3.4 0.94

500 North via 200 West Provo Minor Arterial

45 16,190 3.63 3.4 0.23

SR 89 300 South via 200 West Provo

Minor Arterial

63 14,644 5.31 3.4 1.91

4525 North via Canyon Road ‐ SR 189

Minor Arterial

4 3,367 1.09 2.69 ‐1.6

3650 North via Canyon Road Minor Arterial

14 5,462 3 2.69 0.31

1650 North via Canyon Road Minor Arterial

34 10,185 1.61 3.4 ‐1.79

University Avenue (SR 189) at 1100 North Provo

Minor Arterial

28 9,728 4.53 2.69 1.84

920 South ‐ SR 89 300 South via 500 West

Minor Arterial

5 7,603 1.09 2.69 ‐1.6

300 W via 4800 N ‐ University Ave (SR 189) Provo

Minor Arterial

16 13,059 1.65 3.4 ‐1.75

800 East via Center Street Orem Minor Arterial

17 18,372 1.19 3.4 ‐2.21

137 of 142


Road Functional

Class Total

Crashes

Average AADT

Actual Crash Rates


Difference



Collector 0 1,952 0 2.68 ‐2.68


Collector 17 7,986 1.51 2.68 ‐1.17


Collector 5 3,625 2.1 2.68 ‐0.58


Collector 10 4,580 1.44 2.68 ‐1.24


Collector 1 798 1.53 2.68 ‐1.15


Collector 19 5,353 2.51 2.68 ‐0.17


Collector 2 2,617 1.27 2.68 ‐1.41


Collector 6 3,645 1.55 2.68 ‐1.13




Collector 17 8,292 3.6 2.68 0.92


Orem Boulevard via Center Street Orem

Minor Arterial

57 22,831 2.35 3.4 ‐1.05

SR 189 (University Avenue) at University Parkway (SR 265)

Minor Arterial

67 15,278 4.88 3.4 1.48

Timpview Drive (650 East) via 2200 North

Minor Arterial

62 14,632 3.69 3.4 0.29

1430 North Provo via 900 East Minor Arterial

64 23,713 3.12 3.4 ‐0.28

700 North Provo via 900 East Minor Arterial

76 22,775 4.48 3.4 1.08

Center Street Provo via 900 East Minor Arterial

41 17,272 3.39 3.4 ‐0.01

SR 89 at 900 East Provo Minor Arterial

6 10,332 0.9 3.4 ‐2.5

Center Street via 800 East Orem Minor Arterial

22 10,523 3.67 3.4 0.27

400 South via 800 East Minor Arterial

11 15,607 1.31 3.4 ‐2.09

500 West ‐ SR 189 via 920 South Minor Arterial

14 8,270 3.`51 2.69 0.82

138 of 142


Road Functional

Class Total

Crashes

Average AADT

Actual Crash Rates


Difference



Collector 0 1,952 0 2.68 ‐2.68


Collector 17 7,986 1.51 2.68 ‐1.17


Collector 5 3,625 2.1 2.68 ‐0.58


Collector 10 4,580 1.44 2.68 ‐1.24


Collector 1 798 1.53 2.68 ‐1.15


Collector 19 5,353 2.51 2.68 ‐0.17


Collector 2 2,617 1.27 2.68 ‐1.41


Collector 6 3,645 1.55 2.68 ‐1.13




Collector 17 8,292 3.6 2.68 0.92


800 South via 800 East Orem Minor Arterial

17 15,407 2.02 3.4 ‐1.38

SR 265 University Parkway (BYU Diagonal)

Minor Arterial

70 17,180 5.91 3.4 2.51

500 West (SR 89) via 800 North Minor Arterial

18 9,575 3.82 2.69 1.13

SR 114 at 820 North Minor Arterial

28 6,250 2.61 2.69 ‐0.08

SR 89 (500 West) at Center Street Provo

Minor Arterial

76 11,213 4.87 3.4 1.47

SR 189 via 1860 South Minor Arterial

21 10,857 1.29 3.4 ‐2.11

Center Street Orem via 1200 West

Minor Arterial

11 9,410 2.18 2.69 ‐0.51

400 South Orem via 1200 West Minor Arterial

27 12,730 3.87 3.4 0.47

800 South Orem at 1200 West 2980 turns North

Minor Arterial

28 11,443 4.47 3.4 1.07

400 West via Center Street Orem Principal Arterial

123 27,530 7.7 3.92 3.78

139 of 142


Road Functional

Class Total

Crashes

Average AADT

Actual Crash Rates


Difference



Collector 0 1,952 0 2.68 ‐2.68


Collector 17 7,986 1.51 2.68 ‐1.17


Collector 5 3,625 2.1 2.68 ‐0.58


Collector 10 4,580 1.44 2.68 ‐1.24


Collector 1 798 1.53 2.68 ‐1.15


Collector 19 5,353 2.51 2.68 ‐0.17


Collector 2 2,617 1.27 2.68 ‐1.41


Collector 6 3,645 1.55 2.68 ‐1.13




Collector 17 8,292 3.6 2.68 0.92


1200 West via Center Street Orem

Principal Arterial

79 28,235 2.56 3.92 ‐1.36

SR 114 via Center Street Orem Principal Arterial

68 13,424 9.25 2.74 6.51

SR 89 500 West Provo Principal Arterial

119 28,702 3.57 3.92 ‐0.35

4200 North Provo Principal Arterial

44 26,548 2.52 3.92 ‐1.4

1860 South Provo Principal Arterial

61 22,110 1.81 3.92 ‐2.11

SR 265 BYU Diagonal/University Parkway Orem

Principal Arterial

198 41,910 6.64 3.92 2.72

Center Street Orem Principal Arterial

38 15,753 2.16 2.74 ‐0.58

SR 114 Orem Principal Arterial

243 30,663 9.91 3.92 5.99

500 South via University Avenue Principal Arterial

274 29,979 7.07 3.92 3.15

SR 114 Center Street Provo via 500 West

Principal Arterial

170 27,416 4.88 3.92 0.96

140 of 142


Road Functional

Class Total

Crashes

Average AADT

Actual Crash Rates


Difference



Collector 0 1,952 0 2.68 ‐2.68


Collector 17 7,986 1.51 2.68 ‐1.17


Collector 5 3,625 2.1 2.68 ‐0.58


Collector 10 4,580 1.44 2.68 ‐1.24


Collector 1 798 1.53 2.68 ‐1.15


Collector 19 5,353 2.51 2.68 ‐0.17


Collector 2 2,617 1.27 2.68 ‐1.41


Collector 6 3,645 1.55 2.68 ‐1.13




Collector 17 8,292 3.6 2.68 0.92


800 East Orem via BYU Diagonal Principal Arterial

59 30,623 1.93 3.92 ‐1.99


46 28,598 1.96 3.92 ‐1.96

820 North Principal Arterial

54 10,757 1.74 2.74 ‐1

I 15 via Center Street Principal Arterial

91 24,500 6.78 3.92 2.86

800 South Orem Principal Arterial

183 47,080 3.29 3.92 ‐0.63


277 33,049 9.81 3.92 5.89


59 28,323 1.9 3.92 ‐2.02

1230 North Provo via 500 West Principal Arterial

387 40,170 4.78 3.92 0.86

550 West Orem Principal Arterial

30 35,775 1.13 3.92 ‐2.79

SR 265 1200 South Orem Principal Arterial

92 11,927 4.43 2.74 1.69

141 of 142


Road Functional

Class Total

Crashes

Average AADT

Actual Crash Rates


Difference



Collector 0 1,952 0 2.68 ‐2.68


Collector 17 7,986 1.51 2.68 ‐1.17


Collector 5 3,625 2.1 2.68 ‐0.58


Collector 10 4,580 1.44 2.68 ‐1.24


Collector 1 798 1.53 2.68 ‐1.15


Collector 19 5,353 2.51 2.68 ‐0.17


Collector 2 2,617 1.27 2.68 ‐1.41


Collector 6 3,645 1.55 2.68 ‐1.13




Collector 17 8,292 3.6 2.68 0.92


SR 75 (Road to Ironton) Principal Arterial

96 19,432 2.37 2.74 ‐0.37

2050 West (Geneva Road) SR 114 turns North

Principal Arterial

24 9,710 3.05 2.74 0.31

I 15 University Ave Principal Arterial

133 30,263 2.77 3.92 ‐1.15

Center Street Orem via State Street

Principal Arterial

228 43,935 4.43 3.92 0.51

SR 189 University Avenue via 300 South

Principal Arterial

73 23,710 3.8 3.92 ‐0.12

Main Street Orem Principal Arterial

337 44,999 6.77 3.92 2.85

SR 265 University Parkway (BYU Diagonal)

Principal Arterial

39 22,292 3.07 3.92 ‐0.85

4800 North Provo (Center Street Orem)

Principal Arterial

52 20,623 2.13 3.92 ‐1.79

900 East Provo Principal Arterial

74 13,332 5.02 2.74 2.28

Sand Hill Road Principal Arterial

243 44,117 5.19 3.92 1.27

142 of 142
