2013 On Road Vehicle Classification Study - · PDF file2013 On‐Road Vehicle Classification Study SUMMARY REPORT PARSONS Page i TABLE OF CONTENTS TABLE OF CONTENTS

2013 On‐Road Vehicle Classification Study

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PARSONS

October 2013

2013 On‐Road Vehicle Classification Study SUMMARY REPORT

PARSONS Page i

TABLEOFCONTENTS

TABLE OF CONTENTS ................................................................................................................... i

APPENIDIX ...................................................................................................................................ii

LIST OF TABLES ............................................................................................................................ii

LIST OF FIGURES ..........................................................................................................................ii

INTRODUCTION .......................................................................................................................... 1

Purpose and Need .................................................................................................................. 1

Project Approach .................................................................................................................... 1

Study Advancements .............................................................................................................. 2

FHWA, HPMS AND MOVES CLASSIFICATIONS ........................................................................... 5

Federal Highway Administration (FHWA) Vehicle Classification ........................................... 5

Highway Performance Monitoring System (HPMS) Functional Classification ....................... 8

Motor Vehicle Emission Simulator (MOVES) ........................................................................ 11

REVIEW OF EXISTING DATA AND SOURCES ............................................................................. 12

Introduction .......................................................................................................................... 12

Regional Transportation Commission of Southern Nevada and Clark County .................... 12

Freeway & Arterial System of Transportation...................................................................... 12

Ramp Fraction .................................................................................................................. 13

Nevada Department of Transportation (NDOT) ................................................................... 13

Evaluation of NDOT HPMS Network for System VMT ..................................................... 16

METHODOLOGIES .................................................................................................................... 17

General Methods of Estimating VMT ................................................................................... 17

Non‐Traffic‐Count‐Based Method ................................................................................... 17

Traffic‐Count‐Based Methods .......................................................................................... 19

Traffic‐Count VMT Estimation for Vehicle Classification ..................................................... 20

Overview .......................................................................................................................... 20

Method 1 – Using NDOT Assigned Segment Lengths ...................................................... 21

Method 2 –Vehicle Classification Percentages ................................................................ 22


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Methodology Selection Basis ........................................................................................... 23

FHWA Vehicle Class to MOVES Source Use Type Conversion Methodology ....................... 25

NEW DATA COLLECTION .......................................................................................................... 31

Data Collected ...................................................................................................................... 31

Time Periods / Video Processing .......................................................................................... 31

Locations ............................................................................................................................. 31

FINAL DATA RESULTS ............................................................................................................... 34

LISTOFTABLES

Table 1: Study Advancements .................................................................................................... 4

Table 2: HPMS and MOVES Vehicle Classification Relationship .............................................. 11

Table 3: Data Types Provided by NDOT ................................................................................... 14

Table 4: Functional Road Type of Vehicle Classification Sites ................................................. 14

Table 5: MOVES Source Use Type Definitions ......................................................................... 25

Table 6: Summary of MOVES Road Type Equivalents .............................................................. 26

Table 7: MOVES Source Use Type Assumptions for Clark County, Nevada ............................. 28

Table 8: New Data Collection Site Location List ....................................................................... 32

Table 9: New Data Distribution ................................................................................................ 34

LISTOFFIGURES

Figure 1: FHWA Vehicle Classification ........................................................................................ 7

Figure 2: Roadway Functional Classification in Las Vegas Area ................................................. 9

Figure 3: Statewide Roadway Functional Classification including Clark County ..................... 10

Figure 4: Vehicle Classification Site Locations ......................................................................... 15

Figure 5: Locations of Transfer Stations and Wastesheds in Clark County, Nevada ............... 29

Figure 6: New Data Collection Site Location Map ................................................................... 33

APPENIDICES Appendix 1: New Data Collection

Appendix 2: Macro Execution Steps and Process Method

Data CD – Raw, Macro‐Enabled and Processed Summaries


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INTRODUCTION

Purpose and Need

Parsons was contracted by the Regional Transportation Commission of Southern Nevada

(RTC) to conduct a vehicle classification study for Clark County, Nevada. The study involved

collecting existing and new vehicle count data for development of vehicle distribution

information that is used for input in the Environmental Protection Agency (EPA) MOtor

Vehicle Emission Simulator (MOVES) regional air quality/emissions model. Vehicle

distribution mix information used to generate on‐road mobile source emissions should be

updated approximately every five years based on EPA guidance. The “Project Team” referred

to in this document refers to the RTC, Clark County Department of Air Quality (CCDAQ) and

Parsons. The last time similar work was performed was in 2003 and so the Project Team has

completely updated and refined the process and delivery methodology.

Project Approach

The project consisted of 8 principal subtasks. These are summarized below:

Subtask 1 – Collect existing data. Existing data sources were identified and requests made

for the raw data.

Subtask 2 ‐ Analyze, organize & format existing data. Once the available existing data was

collected, the data was processed and compiled data into formatted, macro‐enabled

spreadsheets.

Deliverable: Output database, method and applicable computer codes, to include count by

FHWA vehicle type, location, date, time (or interval).

Subtask 3 ‐ Develop data conversion methodology for converting FHWA vehicle classification

into a format using MOVES vehicle classifications.

Deliverable: Data conversion methodology in terms of both vehicle count and VMT by HPMS

road type/area (to the extent that existing data is available). Any associated formulas or

code developed by Parsons to produce the conversion are provided.

Subtask 4 – Develop a Data Collection Plan (DCP) to develop the representative split

percentage between passenger car and light duty truck in this study. The split percentages

were determined by representative road types, hours and days of week.


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Deliverable: Data Collection Plan (DCP). The priorities for data required were coordinated

with RTC and CCDAQ for approval before proceeding with data collection.

Subtask 5 ‐ New data collection. New data was collected as described in the Data Collection

Plan from Subtask 4 above.

Deliverable: New data collected as described in the approved DCP of Subtask 4.

Subtask 6 – Provide data summaries of a compiled database with FHWA classifications

converted to MOVES classifications. Passenger car and light duty truck distributions were

calculated using split percentage determined from Subtasks 4 and 5 and these percentages

were applied to the existing data compiled in Subtasks 1 and 2.

Deliverable: Database modified to include MOVES vehicle classifications in terms of both

vehicle count distribution and VMT by HPMS road type/area. Macros to create data

summaries were provided.

Subtask 7 – Prepare draft and final reports and present findings to RTC and CCDAQ. Parsons

assimilated all the tasks and data collected and prepared the draft and final reports.

Deliverable: Draft and final reports.

Subtask 8 ‐ Coordination Meetings. Coordination meetings were conducted as needed for

approval on milestones and development of the milestones.

Study Advancements

As frequently stated in the original study, there were budget limitations on what could be

feasibly accomplished and what might be in accomplished on future studies with additional

funding. The Project Team on this study also faced similar challenges due to the nature of

this type of project. However, because of the considerable advancements made by NDOT

with their available classification and traffic count data, as well as resources allocated for

this study, the Project Team was capable of accomplishing additional tasks and significantly

more data processing and analysis. Although more can always be done toward the goals of

this type of study, these advancements greatly improved the statistical significance of the

results in this study.


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One of the most significant differences between the 2003 study and this current one was the

substantial amount of unique data location points received, analyzed, formatted and

processed as a supplement in this study. For example, the existing classification data

collected with this study involved an evaluation of 67,500 hours of data points compared to

the 1,400 data points in 2003. Additionally, this study included an evaluation and processing

of NDOT HPMS Vehicle Counts (approximately 1,000,000 hours of data) and NDOT’s

Continuous Data (approximately 1,576,800 hours of data), which was not a part of the

original study. In addition to the data collected, the summaries provided were updated and

significantly more comprehensive. This study converted FHWA classification into the EPA’s

latest regional air quality/emissions model, MOVES. It also provided a manual count with

video backup of new bi‐directional data to evaluate and supplement the distribution

between FHWA Class 2 and 3 vehicles, which are typically only estimated by a national or

state algorithm. The new data also provided a count for MOVES source type motor homes to

supplement the FHWA conversion. The 2003 study only provided summaries with daily totals

for 3 of the FHWA road types, based on typically 1 to 2 days of data and with no seasonal

factors developed and applied. The summaries in this current study provided data for 12

FHWA road types. Additionally, because of the factors developed, the summaries calculate

data for every hour of every day of the week for every month, based on 3 years of data.

Below is a summary of the study advancements made in this study compared to the previous

scope. Some recommendations for future studies are made at the end of this report.


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Table 1: Study Advancements STUDY TASK 2003 ORIGINAL STUDY 2013 CURRENT STUDY

Existing Data Collected

Classification Data

Locations 31 short term 67 short term

Road Types 4 10 (no Local Road data)

Direction Single Direction Bi‐Directional

Time Periods 1 year 3 years

1 month 1 month

typically 1 or 2 days 7 days

24 hours 24 hours

Approx. Total Data Points 1,400 hours 67,500 hours

NDOT HPMS Vehicle Counts

Locations None 1000+ short term

Road Types None 10 (no Local Road data)

Direction None Bi‐Directional

Time Periods None 3 years

1 month for short‐term

7 days

24 hours

Approx. Total Data Points None 1,000,000 hours

Continuous Data For Factors (3 Yrs)

Volume & Classification None 9 Locations

Volume only None 21 Locations

Approx. Total Data Points None 1,576,800 hours

Data Processing & Summaries

Data Processing

New Data and Collection Method FHWA Classification using automatic traffic recorders, EPA national conversion factors used to distinguish between Class 2 and 3.

Local split percentages between passenger car and light duty truck were determined using video collection for representative times with manual review of recordings.

NDOT Conversion Factors none Monthly factors from continuous data HPMS factors

VMT Calculation National data provided by EPA, Local information not used.

Local NDOT HPMS data reviewed and processed for evaluation, however, it was determined by NDOT and the project team that an accurate representation of the full system would not be possible within the schedule and budget constraints of this study.

Data Summaries Developed

Vehicle Classification Conversion for EPA Modeling

FHWA to Mobile 6: NDOT provided the required conversion factors from vehicle registration data.

FHWA to MOVES: Methodology developed based off of research and recent TTI approach

Road Functional Classes Covered 4 FHWA types 12 FHWA types

Vehicle classification covered 13 FHWA types, used national default differentiation between Class 2 and 3

13 FHWA types, additional data collected to refine distribution between Class 2 and 3

Seasonal Factors none Monthly

Time periods covered Daily totals Hourly totals, for each day of week and each month of year. Summaries for weekly profiles, hourly by season and weekly by month

Total Combined Data Summaries 5 2088


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FHWA,HPMSANDMOVESCLASSIFICATIONS

Federal Highway Administration (FHWA) Vehicle Classification

The U.S. DOT FHWA Office of Highway Policy Information (OHPI) describes vehicle

classification as the following:

FHWA classifies vehicles into the following 13 classifications:

1. Motorcycles (Optional)—All two or three‐wheeled motorized vehicles. Typical vehicles in

this category have saddle type seats and are steered by handlebars rather than steering

wheels. This category includes motorcycles, motor scooters, mopeds, motorpowered

bicycles, and three‐wheel motorcycles. This vehicle type may be reported at the option of

the State.

2. Passenger Cars—All sedans, coupes, and station wagons manufactured primarily for the

purpose of carrying passengers and including those passenger cars pulling recreational or

other light trailers.

3. Other Two‐Axle, Four‐Tire Single Unit Vehicles—All two‐axle, four‐tire, vehicles, other

than passenger cars. Included in this classification are pickups, panels, vans, and other

vehicles such as campers, motor homes, ambulances, hearses, carryalls, and minibuses.

Other two‐axle, four‐tire single‐unit vehicles pulling recreational or other light trailers are

included in this classification. Because automatic vehicle classifiers have difficulty

distinguishing class 3 from class 2, these two classes may be combined into class 2.

The classification scheme is separated into categories depending on whether the vehicle

carries passengers or commodities. with non‐passenger vehicles are further subdivided

by number of axles and number of units, including both power and trailer units. The

addition of a light trailer to a vehicle does not change the classification of the vehicle.

Automatic vehicle classifiers need an algorithm to interpret axle spacing information to

correctly classify vehicles into these categories. The algorithm most commonly used is

based on the "Scheme F" developed by Maine DOT in the mid‐1980s. The FHWA does not

endorse "Scheme F" or any other classification algorithm. Axle spacing characteristics for

specific vehicle types are known to change from State to State. As a result, no single


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4. Buses—All vehicles manufactured as traditional passenger‐carrying buses with two axles

and six tires or three or more axles. This category includes only traditional buses (including

school buses) functioning as passenger‐carrying vehicles. Modified buses should be

considered to be a truck and should be appropriately classified.

Note: In reporting information on trucks the following criteria should be used:

• Truck tractor units traveling without a trailer will be considered single‐unit trucks.

• A truck tractor unit pulling another such units in a “saddle mount” configuration will

be considered one single‐unit truck and will be defined only by the axles on the

pulling unit.

• Vehicles are defined by the number of axles in contact with the road. Therefore,

“floating” axles are counted only when in the down position.

• The term “trailer” includes both semi‐ and full trailers.

5. Two‐Axle, Six‐Tire, Single‐Unit Trucks—All vehicles on a single frame including trucks,

camping and recreational vehicles, motor homes, etc., with two axles and dual rear wheels.

6. Three‐Axle Single‐Unit Trucks—All vehicles on a single frame including trucks, camping

and recreational vehicles, motor homes, etc., with three axles.

7. Four or More Axle Single‐Unit Trucks—All trucks on a single frame with four or more axles.

8. Four or Fewer Axle Single‐Trailer Trucks—All vehicles with four or fewer axles consisting of

two units, one of which is a tractor or straight truck power unit.

9. Five‐Axle Single‐Trailer Trucks—All five‐axle vehicles consisting of two units, one of which

is a tractor or straight truck power unit.

10. Six or More Axle Single‐Trailer Trucks—All vehicles with six or more axles consisting of

two units, one of which is a tractor or straight truck power unit.

11. Five or fewer Axle Multi‐Trailer Trucks—All vehicles with five or fewer axles consisting of

three or more units, one of which is a tractor or straight truck power unit.

12. Six‐Axle Multi‐Trailer Trucks—All six‐axle vehicles consisting of three or more units, one

of which is a tractor or straight truck power unit.

13. Seven or More Axle Multi‐Trailer Trucks—All vehicles with seven or more axles consisting

of three or more units, one of which is a tractor or straight truck power unit.


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Figure 1: FHWA Vehicle Classification


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Highway Performance Monitoring System (HPMS) Functional Classification

The HPMS provides data that reflects the extent, condition, performance, use, and operating

characteristics of the Nation's highways. It was developed in 1978 as a national highway

transportation system database. It includes limited data on all public roads, more detailed

data for a sample of the arterial and collector functional systems, and certain statewide

summary information. HPMS supports a 1965 congressional requirement that a report on

the condition of the Nation's highway needs be submitted to Congress every two years.

In addition, the HPMS serves needs of the States, MPOs and local government and other

customers in assessing highway condition, performance, air quality trends, and future

investment requirements. Many States rely on traffic and travel data from the HPMS to

conduct air quality analyses and make assessments related to determining air quality

conformity.

HPMS categorizes streets into two area types, Rural and Urban, and further separates these

into eight functional classes for each area, for a total of 16 types, as follows:

HPMS Road Type AreaType RoadType

11 Rural Rural Interstate 13 Rural Rural Other Principal Arterial 15 Rural Rural Minor Arterial 17 Rural Rural Major Collector 19 Rural Rural Minor Collector 21 Rural Rural Local

23 Urban Urban Interstate

25 Urban Urban Other Freeways and Expressways

27 Urban Urban Other Principal Arterial 29 Urban Urban Minor Arterial 31 Urban Urban Collector 33 Urban Urban Local

The classification process is not an exact science and for the purposes of this study, the

Project Team used road functional classification as assigned by NDOT. The map on the next

page shows the Roadway Functional Classification for the majority of Las Vegas area. The

map after that is the Statewide Roadway Functional Classification map including Clark

County.

One of the difficulties surrounding the relationship between highway functional classification and design guidelines is that the classification process is not an exact science. The predominant traffic service associated with a particular route cannot be definitely determined without exhaustive surveys of traffic origin‐destination patterns on each link of the road network. Engineering judgment based on experience must play a role in making design decisions.

‐ FHWA Flexibility in Highway Design Chapter 3: Functional Classification


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Figure 2: Roadway Functional Classification in Las Vegas Area


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Figure 3: Statewide Roadway Functional Classification including Clark County


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Motor Vehicle Emission Simulator (MOVES)

EPA's Office of Transportation and Air Quality (OTAQ) has developed the MOtor Vehicle

Emission Simulator (MOVES). This new emission modeling system estimates emissions for

mobile sources covering a broad range of pollutants and allows multiple scale analysis.

MOVES currently estimates emissions from cars, trucks & motorcycles. Because the HPMS is

a fundamental source of activity information, the MOVES use types are defined as subsets of

the HPMS vehicle classifications. These use types are shown in the table below from the

MOVES Reference Manual.

Table 2: HPMS and MOVES Vehicle Classification Relationship


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REVIEWOFEXISTINGDATAANDSOURCES

Introduction

The Project Team investigated collecting data from several different sources throughout

Clark County. These included the Regional Transportation Commission of Southern Nevada,

CCDAQ, the Freeway & Arterial System of Transportation (FAST) and the Nevada Department

of Transportation (NDOT). The principal data used for this study was supplied by NDOT,

however the following sections describe the interaction from all data sources.

Regional Transportation Commission of Southern Nevada and CCDAQ

The RTC and CCDAQ directed the work performed on this study and also assisted with some

data and background. CCDAQ provided the parameters required for this study and the items

required in the scope of work. A copy of the previous study completed by Orth‐Rodgers &

Associates in 2003 was provided and reviewed. One of the methods for estimating VMT by

vehicle classification was using travel demand forecast modeling. The RTC maintains and

updates this source of information, however, this was not the preferred method decided

upon by the Project Team.

Freeway & Arterial System of Transportation

The team contacted FAST for information on the data capabilities. FAST, administered by the

RTC, is one of the first integrated Intelligent Transportation System (ITS) organizations in the

country. The Nevada Department of Transportation (NDOT) and the RTC are funding

partners, contributing to the operations and management of FAST. The RTC describes FAST

organization and capabilities as follows:

FAST is under the jurisdiction of the RTC elected board, which makes policies

for FAST. Transportation strategies are set by the Operations Management

Committee (OMC), comprised of the RTC, Clark County, NDOT and the cities

of Henderson, Las Vegas and North Las Vegas. RTC staff is responsible for

two major areas that make up the FAST system: the Arterial Management

Section, which includes all arterial streets and roadways; and the Freeway

Management Section, which includes the entire freeway network.


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The project team discussed the project needs with FAST, however it was determined early

on that their data would not be feasibly applied for purposes of this study. FAST has made

some impressive advancement to their systems over the last few years. However, much of

the data is live camera data, and not historical data that would be needed for this study. Also

the type of data that can be acquired from the FAST cameras does not have video backup for

quality checking as required by CCDAQ for this study.

RampFraction

An approach was discussed for the potential for determining “ramp fraction” by capturing

still frames of the video and then recording the information, however the time and effort

estimates by FAST were beyond the budget restraints of the project. Therefore, the Project

Team recommends the EPA national default value be used for this input parameter.

Nevada Department of Transportation (NDOT)

The greatest wealth of data for this study came from NDOT. The team worked closely with

NDOT for several months to collect all the information they had available and to develop an

understanding of their approaches, the purpose of the data and its limitations. The Nevada

Department of Transportation has made significant advancements since the last time this

type of study was conducted in 2003. They consist of several thousand data files each with

numerous parameters involving vehicle count volumes, vehicle classification and road

classification types according to Federal Highway Administration requirements. Background

information and related publications were researched as an initial step of the study. Then

new methodologies were developed to analyze and process the thousands of data files over

a 3‐year historic period.

Various reports produced by NDOT were reviewed as background information to the data

available and methodologies performed by NDOT. These reports include:

FAST is designed to both monitor and control traffic. The traffic control

component of the system consists of freeway and arterial management. Traffic

control requires detection of traffic conditions through the use of video image

detection and inductive loop detection. Visual verification of conditions is

possible through closed‐circuit television cameras. Traffic control is achieved

through the use of traffic signals, ramp meters, dynamic message signs, and


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Annual Vehicle Miles of Travel using Highway Performance Monitoring System,

December 2011

2011 Vehicle Classification Distribution Report by Route, February 2012

NDOT Annual Traffic Reports

The Project Team then collected four types of data from NDOT over a three year period of

time (2010, 2011 and 2012). A summary of these types of data and the details within each

are summarized below. The road functional class assigned to each of the 67 site is shown

below along with a map showing their locations.

Table 3: Data Types Provided by NDOT

Table 4: Functional Road Type of Vehicle Classification Sites

HPMS

Road

TypeID

1 Rural Rural Principal Arterial - Interstate 11 820,188,965 2,247,093 42 Rural Rural Principal Arterial - Other 13 450,932,105 1,235,430 76 Rural Rural Minor Arterial 15 49,309,933 135,096 37 Rural Rural Major Collector 17 90,307,157 247,417 68 Rural Rural Minor Collector 19 46,425,946 127,194 19 Rural Rural Local 21 128,351,078 351,647 0

11 Urban Urban Principal Arterial - Interstate 23 2,728,884,522 7,476,396 312 Urban Urban Principal Arterial - Other Freeways 25 1,274,595,874 3,492,043 214 Urban Urban Principal Arterial - Other 27 1,938,904,478 5,312,067 1516 Urban Urban Minor Arterial 29 3,612,857,515 9,898,240 2017 Urban Urban Collector 31 1,597,692,394 4,377,239 619 Urban Urban Local 33 2,041,398,518 5,592,873 0

Total 14,779,848,485 40,492,736 67

FHWA Road Type AreaType RoadType 2010 AAVMT 2010 ADVMT

Num. of Sites

DATA TYPE DESCRIPTION

67 SITES

480 FILES, provided on CD2010, 2011, 2012 Bi‐directional data

7 cont. days, 24 cont. hours 13 FHWA vehicle types

30 CONTINUOUS SITES 365 days, 24 hrs

9 locations provide continuous data for volume and classification 21 locations more, provide just volume

HPMS 1000+ SITES

7 days, 24 hrsBi‐directional numbers

Lists AM/PM: Peak volume, hour and factor NDOT Developed Factors, Factored AADT

Expansion Factors Seasonal factors developed by NDOT from the continuous data sites


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Figure 4: Vehicle Classification Site Locations


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EvaluationofNDOTHPMSNetworkforSystemVMT

The RTC and CC requested that all data available from NDOT be reviewed for possible

application on this study. The “1000+ Site” data of one week traffic counts were provided for

the three years of analysis (2010, 2011, 2012). After receipt of the files, it was discovered

that the 1000‐site data and the number of traffic count sites varied from year to year. The

project team formatted and processed this information to determine total vehicle counts

which was then multiplied by the reported segment lengths in an attempt to replicate

NDOT’s HPMS reported annual VMT for the complete system. After processing these

calculations, it was discovered that there was a very significant discrepancy in the values

calculated through this study effort and the actual values published by NDOT.

This discrepancy led to further discussion with NDOT staff to investigate the discrepancy and

attempt to resolve it. This would also require resolving the discrepancies between the

varying number of records related to the differing number of unique stations, which also

varied more relative to each year. At this point, NDOT explained to the study team members

the significant challenge involved with this effort and the limitation of what could be

accomplished with the data considering the project resources and timeline.

It was determined that replicating NDOT’s system VMT would not be feasible with the

resources and time constraints of this study. However, the 1000‐site data was still used for

the purpose of developing additional factors to be applied with the overall data.


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METHODOLOGIES

General Methods of Estimating VMT

During the development of this study, different methodologies were reviewed to carry out

the project goals. The Project Team reviewed methodologies for estimating vehicle miles

traveled (VMT) for all vehicles and for specific vehicle classifications as well.

Estimates of VMT are used extensively in transportation planning for allocating resources,

estimating vehicle emissions, computing energy consumption, and assessing traffic impact.

The estimates used in these applications usually come from different sources.

Vehicle miles traveled is defined as the total miles traveled by all vehicles on a road network.

In order to produce a true VMT count, accurate information on the total miles driven by all

vehicles travelling within the road network is needed. However, because this is not feasible,

different methods have been developed to estimate VMT totals as best as possible with

resources. Each of these VMT methods will most likely generate different values for the

same network and same time periods, so agencies will need to choose the best fit for their

purpose and available resources.

The following VMT estimation methods are generally used by state DOT’s:

Highway Performance Monitoring System (HPMS),

Highway/transit network models,

Fuel sales,

Odometer recordings, and

Household and driver surveys

VMT estimation methods can be separated into two general categories: non–traffic count

based and traffic count based. Although this study utilizes traffic count methods, the Project

Team discussed different methods and so each is briefly discussed in the following sections.

Non‐Traffic‐Count‐BasedMethod

Non‐traffic‐count‐based VMT estimation methods utilize data from alternate sources of

information not coming from traffic counts. These can include socioeconomic data sources

such as fuel sales, trip‐making behavior, household size, household income, population,

number of licensed drivers, and employment. Because these databases are expensive to fully


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maintain, only rough updates are typically performed, which can sometimes create less

desirable accuracy in the results.

Travel Demand Forecasting Models

Travel demand models project regional traffic and forecast link volumes. Ground counts are

used to calibrate base year estimates and then volume projections are made for future

scenarios. VMT estimates are obtained from the product of the forecasted link volumes and

the respective centerline mileage of the link. Output from travel demand forecasting models

is also used for estimations. One of disadvantages with this approach is that the models

often lack the necessary data. The accuracy of the output volumes depends on trip

generation and distribution components and the representativeness of the network to the

actual street system.

Fuel Sales

This method estimates VMT from fuel sales. The total amount of fuel purchased in an area is

calculated by dividing fuel sales for retail gasoline and diesel by the unit price per gallon. The

number of miles traveled per gallon of fuel is determined based off of estimates of fuel fleet

efficiency. Then VMT is calculated using a standard equation. However, there can be wide

variations of fuel efficiency for vehicles and errors can easily result from the inaccurate

estimates of retail fuel sales and prices. Additionally, it is difficult to distribute VMT between

residents and non‐residents.

Odometer Recordings

Some vehicles, such as certain trucks, record annual odometer readings during registration.

These values can be used to indicate the VMT. Actual VMT may be obtained by summing the

odometer recordings for all vehicles, assuming that there are no errors associated with the

odometers. However, this would require that odometer readings are available for all

vehicles. VMT estimation using odometer readings is too resource intensive, probably the

main reason that it is not often used. The method has a number of other potential sources of

error, such as:

Odometer calibration errors due to worn‐out odometer cables

Reporting errors

Second‐party readings or transcription errors

Odometer rollovers

Odometer tampering


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Vehicle drop‐out caused by accidents or aged vehicles

Out‐of‐area travel likely to be considered in‐area travel

Household and Driver Survey Data

The FHWA OHPI states, “The National Household Travel Survey (NHTS) is a periodic national

survey, to assist transportation planners and policy makers who need comprehensive data

on travel and transportation patterns in the United States. The 2009 is the latest survey

collected by FHWA, in coordination with a private firm who conducted the survey around

the country. Previous surveys included the 2001 NHTS, and the former Nationwide Personal

Transportation Surveys (NPTS) of 1969, 1977, 1983, 1990, and 1995. The NHTS/NPTS serves

as the nation's inventory of daily travel. Data is collected on daily trips taken by households

and individuals in those households, over a 24‐hour period, and includes:

• Purpose of the trip (work, shopping, social, etc.)

• Means of transportation (car, walk, bus, subway, etc.)

• Travel time of trip

• Time of day/day of week

These data are collected for all trips, modes, purposes, trip lengths, and all areas of the

country, urban and rural.”

Although not originally intended for VMT estimation, some have developed cross‐modeling

so that information from the NPTSs have been used to forecast VMT. NPTS data were used

to determine annual VMT per driver, distribution of licensed drivers, and trends in VMT per

driver. However, this approach is also only a sampling method dependent on the accuracy

and coverage of the data collected and the key assumptions that need to be made.

Traffic‐Count‐BasedMethods

Highway Performance Monitoring System (HPMS) Method

FHWA’s traffic count–based HPMS method is the most accepted method for estimating VMT

in the United States because it is based on actual data for vehicle movement. The HPMS

method is also the Environmental Protection Agency’s preferred method under Section 187.

Daily VMT for a sample section of road is calculated by multiplying the adjusted full day

traffic counts by the centerline mileage of that sample section. This value is annualized by

multiplying by the number of days in the year. Sample section VMT is used to approximate

area wide VMT.


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The HPMS method also involves the use of continuous count stations to develop expansion

factors used to adjust counts to represent daily and monthly traffic patterns. Continuous

counts are performed using permanent counters which collect traffic data continuously for

24 hours a day, 365 days a year. They are used to assist understanding the time‐of‐day, day‐

of‐week, and seasonal travel patterns and to facilitate the development of seasonal

expansion factors required to convert short‐term counts to accurate estimates of AADT.

The accuracy of this type of VMT estimation is dependent on the accuracy of the traffic data

and mileage collected, and the coverage of the road network. If coverage counts were

available for all roads in a network, the VMT estimate obtained would be the best possible

estimate; however, in most cases that is not feasible and only portions of the network are

sampled to obtain data.

Additionally, some key assumptions are made with this estimation method. For example, it is

customary to assume that vehicles counted on a sample section of the road traveled the

entire length of that section. In reality some vehicles will travel only a portion of the sample

section because they will either enter or leave at different access points within the section.

Some vehicles will be counted while others will not, depending on whether they cross the

counting location. Carefully selected sample sections can help reduce the potential margin of

error from this assumption.

This method of estimating VMT is presently the most preferred by state DOTs as it utilizes

actual data of vehicle movement on a road segment. About 70% of state DOTs, including

NDOT, use a traffic‐count‐based method.

Traffic‐Count VMT Estimation for Vehicle Classification

Overview

Vehicle classification VMT is typically calculated using two different traffic count‐based

methods. In one method (Method 1), VMT is estimated on a road segment basis by

multiplying the vehicle class counts by the roadway section length (centerline mileage). The

second method (Method 2) uses the HPMS method described above to determine the total

VMT and then it estimates vehicle classification VMT by multiplying the total by an average

percentage for each specific class. This can be broken down further by road function classes.

The best possible VMT estimates would be those obtained using the traffic‐count‐based

method if all road sections of interest are monitored continuously throughout the year to

produce AADT. Resource constraints, however, make it impractical for the collection of


PARSONS Page 21

traffic count data on all sections of interest. Hence, data are collected continuously at a

limited number of count locations, while other locations are counted only at infrequent

intervals. To account for the temporal variations in short‐duration traffic counts, data from

sites that are counted continuously are used to develop expansion factors for factoring

short‐duration counts to estimates of annual average daily traffic (AADT).

The Project Team evaluated versions developed from both these methods above as an

approach for meeting the goals of this study. Both methods are presented below with a

summary of method selection reasoning.

Method1–UsingNDOTAssignedSegmentLengths

The first method presented generally involved using count data from the 67 vehicle

classification sites and multiplying each vehicle class by their assigned segment lengths to

obtain VMT for each vehicle class. These would then be used to calculate VMT distribution

percentages for each vehicle class, for every hour, of typical weekdays for every month of

the year and for each road and area type. The NDOT‐provided expansion factors would be

used for extrapolating the data for every month of the year. Below is a general outline of the

process for this methodology:

1. Group 480 files of 67‐site short‐term classification into the 12 HPMS road types

2. Combine volumes from both directions for segment total

3. Factor all volumes to the same month (for example January) using factors calculated

from the 30+ continuous sites

4. Combine factored volumes from 3 years (2010, 2011, 2012) and calculate average

volumes for January

5. Calculate segment lengths from NDOT table (or use NDOT provided values)

6. Calculate VMT by multiplying volumes by site segment length (for every hour, for 7‐

day period in January)

7. Sum all segment VMTs within each road type (for every hour, for 7‐day period in

January) for a total representative VMT for the system road type

8. Calculate Percent VMT within each road type (for every hour, for 7‐day period in

January

9. Repeat process for all 12 months

The above methodology was demonstrated using actual data provided from NDOT by

calculating the Percent VMT for all vehicle types within the Urban Interstate road‐type for a

Sunday in January at 12am.


PARSONS Page 22

Method2–VehicleClassificationPercentages

Overview

The second method presented generally involved using count data from the 67 vehicle

classification sites to calculate volume distribution percentages for each vehicle class, for

every hour, of typical weekdays for every month of the year and for each road and area

type. The 30‐site continuous volume data would be used to develop monthly factors and the

1000‐site short term volume data would be used to develop week and hourly factors.

Below is a general outline of the process for this methodology:

1. Use the 30 continuous count station data to determine monthly factors by

facility/area type.

2. Use the 1000 site data to determine for each month the day of the week and

hourly VMT fractions by facility/area type.

3. Use the 67 site data to determine FHWA vehicle fractions by facility/area type.

4. Use NDOTs HPMS VMT as the control totals against which the above “fractions”

are applied.

Because of the very large number of files and data points and the inconsistent relative

formats, advanced programming was used to carry out this methodology process. A set of

separate “Excel Enabled Macros” (See Appendix 2 for additional detail) was developed to

execute each of the following substeps:

30‐Site Files (365‐day continuous volume data)

Starting with one year of data, format all the input files to compute monthly factors by

function class and by month. Repeat the process for each year of the three year data and

calculate averages of the three years. The final file of this data is the percent share of month

volume data by functional class.

1000‐Site Files (Short‐term volume data)

Starting with one year of data format all the input files to compute the VMT at the count

sites by week of the day and by hour for each month. The link segment lengths associated

with the count sites were used in the VMT calculation. If the segment length was missing,

then the average segment length by Facility type was used. Repeat the process for each year

of the three year data and calculate averages of the three years. The final file of this data is

the percent share of VMT by hour, by week of day, by month volume.


PARSONS Page 23

67‐Site Files (Short‐term classification data)

Starting with one year of data format all the input files to compute the vehicle classification

by road functional class. Repeat the process for each year of the three year data and

calculate averages of the three years. The final file of this data is the present share of

vehicle class by hour and by road functional class. Note that this data was grouped by season

due to limited data availability (Summer – May, June, July and August. Winter – November,

December, January and February. Shoulder – March, April, September and October).

NDOTs HPMS VMT as Control Totals

The request in this study was not to produce final VMT totals for each vehicle classification,

but rather the percent distribution of each. However, at this point VMT totals for each

vehicle class could be calculated by applying those percentages to NDOTs HPMS calculated

VMT. The results of this study are VMT share factors by vehicle class, by road facility

functional class, by hour, by week of the day, and by month.

MethodologySelectionBasisforUsingthe67‐SiteFiles

Method 1 focused on producing an hourly breakdown of VMT percentages for each vehicle

class, rather than estimated VMT counts. Method 2 focused on providing an hourly

breakdown of percentages for each vehicle class, which could then be applied to the HPMS

system for a vehicle class VMT by road type.

Both methods are based off of methods/studies used by FHWA/HPMS and other states,

however, below is a summary of how Method 1 compares to Method 2, and was used for

the selection between the two.

Method 1 Advantages over Method 2:

Method 1 more directly provided the data result in the format requested for this

study.

Method 1 accounts for the difference between volume‐based distribution and

length‐based distribution.


PARSONS Page 24

Method 1 Disadvantages over Method 2:

• Ideally the whole system should be covered for Method 1 VMT calculations.

However, this is highly resource‐intensive and some states may use traffic classifiers

not covering the system, which provides less quality results.

The quality of the VMT calculation results is completely reliant on the validity of the

segment length for vehicle classification determination. There were no known

studies conducted to validate the NDOT segment lengths for the purpose of VMT

calculation and therefore using these in the study may create arbitrary weighting of

the results.

• A sample size of 67 sites is also a statistically small quantity for this method

approach. The number of sites available for each road type range from 0 (local roads)

to 20 (Urban Minor Arterial).

The limitations from both sample size and the segment length validity would provide less

quality data results than using volume based ‐ which could then be applied to a full system

network of VMT calculated by NDOT. 67 sites is not enough to produce accurate

representation for the system and the segment lengths assigned may skew the data

incorrectly. These two disadvantages outweigh the advantages and Method 2 was selected

as the process for producing the study results.


PARSONS Page 25

FHWA Vehicle Class to MOVES Source Use Type Conversion Methodology

The methodology described herein is based on research undertaken by the Texas

Transportation Institute (TTI) as described in a documented entitled “Methodologies for

Conversion of Data Sets for MOVES Model Compatibility” dated August 2009. Repetition of

the research paper’s contents is minimized to the extent possible.

As with the TTI paper, no attempt is made to correlate FHWA vehicle classification count

data with MOBILE6 or MOBILE6 with MOVES. This methodology focuses on the conversion of

FHWA vehicle class count data, as collected and reported by NDOT, directly with MOVES

source use types, or “bins.” Unique source bins are defined by vehicle and fuel types with

the largest influence on energy consumption and emissions. Table 5 below shows the

MOVES source use types and a general correlation to the various FHWA vehicle

classifications. This table is a reproduction of Table 3 as reported in the TTI research paper.

Table 5: MOVES Source Use Type Definitions

FHWA CLASS MOVES SOURCE USE

TYPE DESCRIPTION

Passenger Cars 21–Passenger Car All

Other 2-axle/4-tire Vehicles 31–Passenger Truck Mini-van, pick-up, etc., used primarily for personal transportation.

32–Light Commercial Truck Mini-van, pick-up, etc., used primarily for commercial applications. Different annual mileage and hours of operation.

Single-unit Trucks 51–Refuse Truck Garbage and recycling trucks. Different schedule, roadway, and hours of operation.

52–Single-unit Short Haul Single-unit trucks with the majority of operation within 200 miles of home base.

53–Single-unit Long Haul Single-unit trucks with the majority of operation outside of 200 miles of home base.

54–Motor Home All

Buses 41–Inter-city Bus City-to-city buses. Not transit or school buses.

42–Transit Bus Buses used for public transit.

43–School School and church buses.

Combination Trucks 61–Combination Short Haul Combination trucks with the majority of operation within 200 miles of home base.

62–Combination Long Haul Combination trucks with the majority of operation outside of 200 miles of home base.

Motorcycles 11–Motorcycle All


PARSONS Page 26

The TTI methodology relies on use of vehicle registration data, observations, and MOVES

default vehicle fractions. A number of FHWA vehicle classes are defined as “nominal” insofar

as being unique to regional conditions. To the extent possible, this (Parsons’) assessment

strives to utilize data specific to Clark County, Nevada to the extent possible.

Toward that endeavor, vehicle classification data has been collected and summarized by

HPMS roadway functional classification—12 types. For the purpose of MOVES, these 12

functional classifications are grouped into five road types, as indicated on Table 6 below.

This table is a reproduction of Table 6 as reported in the TTI research paper. For the purpose

of this study, however, no grouping of roadway classifications was taken.

Table 6: Summary of MOVES Road Type Equivalents

Group MOVES SOURCE USE TYPE HPMS ROAD FUNCTION CLASS

1 2‐Rural Restricted Access 1–Rural Interstate

2 3–Rural Unrestricted Access 2–Rural Principal Arterial (other)

6–Rural Minor arterial

7–Rural Major Collector

8–Rural Minor Collector

9–Rural Local

3 4–Urban Restricted Access 11–Urban Principal Arterial (interstate)

12–Urban Principal Arterial (other freeway)

4 5–Urban Unrestricted Access 14–Urban Principal Arterial (other)

5 5–Urban Unrestricted Access 16–Urban Minor Arterial

17–Urban Collector

19–Urban Local

FHWA vehicle class conversions to MOVES source use types have been calculated or

assumed. These assumptions are summarized in Table 7 and are discussed below.

Passenger Cars. This is a one‐for‐one conversion between FHWA Class 2 and MOVES source

type 21. The traffic count fractions will be used for all days of the week, hours of the day,

and months of the year.

Other 2‐axle/4‐tire Vehicles. FHWA Class 3 vehicle fractions will be split between MOVES

source types 31, Passenger Trucks, and 32, Light Commercial Trucks, based on new data

collections as described later in this document.


PARSONS Page 27

Single‐unit Trucks. FHWA classifications 5, 6, and 7 comprise single‐unit trucks, divided by

four MOVES source use types. Type 51, Refuse Truck, is assumed to be a local collection

FHWA Class 6 vehicle traveling to a transfer station, where recyclables are sorted from land

fill material. Three transfer stations are assumed, as illustrated on Figure 5, along with their

respective “wastesheds.” Each housing unit is estimated to produce 5.2 tons of trash

annually, and a recycling rate of 20 percent is assumed. The trash collection to transfer

station route is assumed to traverse type 1, 3, and 5 MOVES class roadways, whereas trips

from the transfer station to the Apex Landfill traverse interstate roadways, types 2 and 4.

Trash collection is limited to weekdays during daylight hours, from 7:00 a.m. to 6:00 p.m.

Vehicle miles traveled (VMT) is computed to be 25,550 per weekday, 52 weeks per year

along Class 1, 3, and 5 roadways. Vehicle miles traveled along Class 2 and 4 roadways

between the transfer stations and Apex Landfill, is computed to be 25,700 carried by type 61

combination short‐haul trucks. This latter VMT is negligible, however, compared with the 9.7

million vehicle miles traveled on interstate highways in Clark County daily. These VMT

computations are approximate target values.


PARSONS Page 28

Table 7: MOVES Source Use Type Assumptions for Clark County, Nevada

FHWA CLASS MOVES SOURCE

USE TYPE DESCRIPTION

FHWA CLASS

NO.

MOVES ROAD TYPE

USE RESTRICTIONS VMT CAP PER DAY

VMT FRACTION/ NOTES DAYS HOURS MONTHS

Passenger Cars

21–Passenger Car All 2 All None None None None All

Other 2-axle/ 4-tire Vehicles

31–Passenger Truck Mini-van, pick-up, etc., used primarily for personal transportation.

3 All None None None None By observation or MOVES default

32–Light Commercial Truck

Mini-van, pick-up, etc., used primarily for commercial applications. Different annual mileage and hours of operation.

3 All None None None None By observation or MOVES default

Single-unit Trucks

51–Refuse Truck Garbage and recycling trucks. Different schedule, roadway, and hours of operation.

6 1, 3, 5 M–F 0700–1800

None 26,000 All class 6 on 1, 3, 5 road types 26,000 VMT maximum

52–Single-unit Short Haul

Single-unit trucks with the majority of operation within 200 miles of home base.

5–7 1, 3, 5 None None None None All Class 5 and 7; residual Class 6 fraction

2, 4 None None None None 45% or MOVES default

53–Single-unit Long Haul

Single-unit trucks with the majority of operation outside of 200 miles of home base.

5–7 2, 4 None None None None 45% or MOVES default

54–Motor Home All 5 2, 4 None None None None 10% or MOVES default

Buses 41–Inter-city Bus City-to-city buses. Not transit or school buses.

4 2, 4 None None None None All

42–Transit Bus Buses used for public transit. 4 1, 3, 5 None None None 45,000 Controlled to VMT cap

43–School School and church buses. 4 1, 3, 5 M–F Peak periods

Late Aug– early June

117,000 Controlled to VMT cap

Combination Trucks

61–Combination Short Haul

Combination trucks with the majority of operation within 200 miles of home base.

8–12 1, 3, 5 None None None None All

2, 4 None None None None 40% or MOVES default

62–Combination Long Haul

Combination trucks with the majority of operation outside of 200 miles of home base.

8–12 2, 4 None None None None 60% or MOVES default

Motorcycles 11–Motorcycle All 1 All None None None None All

Note: Trial and error calculations required for specified VMT fractions MOVES source use types 52, 53, 54, and 61, 62.


PARSONS Page 29

Figure 5: Locations of Transfer Stations and Wastesheds in Clark County, Nevada


PARSONS Page 30

The remaining three MOVES source use types of single‐unit trucks are unrestricted insofar as

day, time, and month. For lack of more definitive data, all movements of FHWA Class 5

through 7 trucks on non‐interstate routes are assumed to be short haul. Insofar as interstate

travel, VMT fractions are assumed to be 45 percent short haul, 45 percent long haul, and 10

percent motor home. The county may prefer to assume MOVES default fractions for these

source use types.

Buses. Buses are FHWA vehicle type 4 and MOVES source use types 41–Intercity, 42–

Transit, and 43–School. Intercity buses have no day, hour, month use restriction, and are

assumed to traverse interstate Type 2 and 4 roadways. Transit and school buses are

assumed to traverse MOVES road types 1, 3 and 5. Transit buses have no day, hour, or

month use restrictions. Vehicle miles traveled are capped at 45,000 per AADT (average

annual daily traffic), based on reported revenue vehicle miles plus five percent for deadhead

movements (to and from the garage). School buses are assumed to operate on weekdays,

during peak commute hours, 180 days per year, from late August to early June. Vehicle miles

traveled are capped at 117,000 per weekday while schools are in session based on reported

annual mileage data. These VMT computations are approximate target values.

Combination Trucks. Combination trucks are FHWA Class 8 through 12 vehicles divided into

short haul and long haul MOVES source use types. The short haul combination trucks are

assumed to dominate truck count observations along MOVES road types 1, 3, and 5. Along

interstates, MOVES road types 2 and 4, the assumed vehicle mix is 40 percent short haul and

60 percent long haul, or the MOVES default activity parameters. No day, time, or month use

restrictions are assumed.

Motorcycles. This is a one‐for‐one conversion between FHWA Class 1 and MOVES source

type 11. The traffic count fractions will be used for all time periods.

The above assumptions appear logical with the exception of short haul versus long haul

single‐unit and combination truck activity parameters. The TTI research paper notes that

nationally, single‐unit short haul trucks generate 92 percent of the VMT for that source use

type compared with eight percent for single‐unit, long haul trucks. Table 3 of this document

notes that trial and error calculations will be required to verify or compare Clark County VMT

fractions with national values. Insofar as combination short haul and long haul trucks, the

VMT national proportions are 50 percent each.


PARSONS Page 31

NEWDATACOLLECTION

The Project Team developed a Data Collection Plan to enhance the information available in

the existing data system. This involved selecting new sites for logging information regarding

vehicle counts and classification. The methods involved video recording and manual

processing. After the new data was collected, it was integrated into the system of existing

data to enhance the results of the study.

Data Collected

The following new data was collected to supplement this study.

1. Class 2 ‐ Passenger Cars ‐‐ All sedans, coupes, and station wagons manufactured primarily for the purpose of carrying passengers and including those passenger cars pulling recreational or other light trailers. (Note ‐ including all small "box shaped" passenger cars having SUV look‐like body shape, and similar or smaller than Sedan in size).

2. Class 3 ‐ Other Two‐Axle, Four‐Tire Single Unit Vehicles ‐‐ All two‐axle, four‐tire, vehicles, other than passenger cars. Included in this classification are pickups, panels, vans, and other vehicles such as campers, motor homes, ambulances, hearses, carryalls, and minibuses. Other two‐axle, four‐tire single‐unit vehicles pulling recreational or other light trailers are included in this classification.

(Note ‐ including all SUVs & jeep‐type vehicles).

3. MOVES Motor Homes MOVES Source Use Type 54‐Motor Homes (FHWA Class 5) FHWA Definition Class 5. Two‐Axle, Six‐Tire, Single‐Unit Motor Homes (dual rear wheels).

4. All other vehicles and total vehicle count

Time Periods / Video Processing

Days: Thursday, Friday, Saturday, Sunday

Hours: 4 hour processed video hour per location:

AM Peak (7am‐8am), mid day (10‐11am), PM Peak (5pm‐6pm), evening (7pm‐8pm)


PARSONS Page 32

Locations

15 total locations, data collected from both traffic directions on each road:

2 rural freeways, 4 urban freeways, 7 urban arterials, 2 urban collectors.

See map on following page with roadway class‐color coded site locations.

Table 8: New Data Collection Site Location List

NO. ROADWAY FUNCTIONAL CLASS CODE STATION LOCATION

1 Rural Principal Arterial ‐ Interstate 11 0035360 IR‐15 1.5 mi S of SR 739 (Sloan Intch)

2 Rural Principal Arterial ‐ Other 13 0033150 US‐93 0.75 mi S of US‐95 Intch

3 Urban Principal Arterial ‐ Interstate 23 N/A IR‐215 at Decatur

4 Urban Principal Arterial ‐ Interstate 23 0031230 IR‐15 0.2 mi N of E Carey Blvd overpass

5 Urban Principal Arterial ‐ Interstate 23 0035340 IR‐15 1.0 mi S of SR 160 (Blue Diamond Rd Intch)

6 Urban Principal Arterial ‐ Other Freeways 25 0030825 US‐95 btwn the Russell Intch 'Exit 65' and the Sunset Intch 'Exit 64'

7 Urban Principal Arterial ‐ Other 27 0030351 SR‐595 (Rainbow Bl) .1 mi N of SR‐593 (Tropicana Av)

8 Urban Principal Arterial ‐ Other 27 0031035 SR‐574 (Cheyenne Av) .2 mi W of Simmons St.

9 Urban Minor Arterial 29 0030563 SR‐589 (E Sahara Av) .1 mi E of Boulder Hwy

10 Urban Minor Arterial 29 0031126 Jones Bl .5 mi N of SR‐147 (Lake Mead Bl)

11 Urban Minor Arterial 29 0035250 SR‐592 (Flamingo Bl) 0.1 mi E of Decatur Bl

12 Urban Minor Arterial 29 0030007 SR‐562 (Sunset Rd) .2 mi W of Pecos Rd

13 Urban Minor Arterial 29 0030366 SR‐599 (Rancho Rd) .3 mi S of SR‐573 (Craig Rd).

14 Urban Collector 31 0030766 Koval Ln 500ft S of SR‐592 (Flamingo Rd)

15 Urban Collector 31 0030961 Sandhill Rd .1 mi N of Patrick Ln

Rural Freeways

Urban Freeways

Urban Arterials

Urban Collectors


PARSONS Page 33

Figure 6: New Data Collection Site Location Map


PARSONS Page 34

FINALDATARESULTS

The Project Team assimilated all existing data and new data and produced summary results

included in an Appendix CD.

After the new data was collected, the hourly vehicle

counts from the four collected hours were distributed

as shown in the adjacent table to cover a full 24‐hour

period. Also Thursday was used to represent all

remaining weekdays (Monday, Tuesday and

Wednesday). Then Class 2 and 3 percentage splits

were calculated for each of the locations and for each

hour. The video was also reviewed to exclude any data

impacted by weather, accidents or other possible

issues. This impact summary, raw data and the

calculated formatted data are included in Appendix 1.

The new data was integrated into the results by

consolidating existing FHWA Class 2 and 3 counts

provided by NDOT into one total and then

redistributing this amount by the percentages

developed with new data calculations, applying them

to typical periods of the day, days of the week and for

respective functional road types. The process involves

using the new video classification data output as a

lookup table to apply estimated actual split percentages of Class 2 versus Class 3 to NDOT

Class 2 and 3 totals. Any time periods not having new data available to be applied used the

default FHWA counts provided by NDOT. Then the FHWA vehicle classes were converted to

MOVES Source Use Type using the methodology described earlier in this report. New data

collected on FHWA Class 5 Motor Homes was also used to support this conversion.

After the final data was integrated and processed, “Macro‐Enabled Spreadsheets” with the

code necessary to produce the results were provided. The results on the Appendix CD

include macros that will produce separate files to represent each 24 hours for each 7

weekdays for all 12 months of the year. The macro execution steps and process are included

in Appendix 2.

Hour

Collected Hour

Assignment

0 - 11 - 22 - 33 - 44 - 55 - 66 - 77 - 88 - 9

9 - 1010 - 1111 - 1213 - 1414 - 1515 - 1616 -1717 - 1818 - 1919 -2020 - 2121 - 2222 - 2323 - 24

Evening (7pm - 8pm)

AM Peak ( 7am - 8am)

Mid-day (10am -11am)

PM Peak (5pm - 6pm)

Evening (7pm -8 pm)

Table 9 – New Data Distribution

Documents

2013 On Road Vehicle Classification Study - · PDF file2013 On‐Road Vehicle Classification Study SUMMARY REPORT PARSONS Page i TABLE OF CONTENTS TABLE OF CONTENTS