Spatial Modeling for Highway Performance Monitoring System...

Spatial Modeling for Highway Performance Monitoring System Data: Part 1

Tuesday, February 27, 2018

2:00-4:00 PM ET

TRANSPORTATION RESEARCH BOARD

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Credit earned on completion of this program will be reported to RCEP. A

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Purpose Discuss how to incorporate spatial modeling and statistical tools to enhance the quality and productivity of travel monitoring data.

Learning Objectives At the end of this webinar, you will be able to: • Identify Highway Performance Monitoring System (HPMS) traffic data

needs and requirements • Describe the linear referencing system and its use in HPMS processing • Describe cardinal and spatial joins through attribute, connectivity,

proximity, and similarity

TRB WEBINAR: SPATIAL MODELING FOR HIGHWAY PERFORMANCE MONITORING SYSTEM DATA PART 1 - FEBRUARY 27, 2018

Maaza Christos Mekuria, PhD, PE, PTOE Dan P. Seedah, PhD

1

WEBINAR OUTLINE

Overview of HPMS

Description of HPMS Travel Data Items

Relationship between HPMS Data items

Linear Referencing System

Sample Panels and HPMS items

Leveraging Spatial Analysis

Q & A session

2

HIGHWAY PERFORMANCE MONITORING SYSTEM (HPMS)

A national level highway information system that provides data on the extent, condition, performance, use and operating characteristics of the nation's highways.

[Ref. FHWA]

3

A PRODUCT OF THE STATEWIDE DATA COLLECTION PROGRAM

[Adapted from FHWA - CPI Manual 2001]

Planning Process Statewide Planning

Project Prioritization and Funding Local/Regional Planning

Corridor Studies, ITS Strategies Freight Planning

Data Collection Programs Management Systems

Inventory, Condition, Travel

Environmental Analysis Engineering Applications

HPMS

4

HPMS CONTINUOUS PROCESS IMPROVEMENT

[Adapted from FHWA - CPI Manual 2001]

Identify At-Risk Areas Select

Review Type

Analyze Processes

Review Guidelines

Outline Current Process

Make Recommend

ations

Prepare Implementat

ion Plan

Follow-up

Measure Outputs

HPMS as a

Key Support Process

- Reauthorization, Appropriations, etc. - Performance Planning

- Congestion Modeling & Safety

5

SUGGESTED STATE HPMS PROCESSING CYCLE

[HPMS Field Manual, 2016]

6

GENERALIZED HPMS PROCESS

Inventory Data Collection

Data Compilation

Information Derivatives

Information Presentation

7

HI HPMS FED. AID ROUTES - 2016

Island NHS Miles

8

HPMS DATA ITEMS

Route – Linear Reference Systems (LRS)

Inventory – Signs, etc.

Geometric – Curve, Grades

Pavement – Distress, Transportation Performance Management

Traffic – Counts, Travel Time (new)

9

HPMS DATA ITEMS

[FHWA HPMS Manual 2016]

10

HPMS DATA ITEMS

[FHWA HPMS Manual 2016]

11

SAMPLE PANEL DATA ITEMS

[FHWA HPMS Manual 2016]

12

LINEAR REFERENCE SYSTEM (LRS)

A system where features (points or segments) are localized by a measure along a linear element.

[Introducing the Linear Reference System in GRASS, 2004]

MP 0 MP 5 MP 10 MP 15

MP = mile point

MP 3.5 MP 6.5

13

LINEAR REFERENCE SYSTEM (LRS)

Segmented : Geometry – Intersection based

Route-level : Spatial Topology

Combined: Link-Node Network

14

LINEAR REFERENCE SYSTEM (LRS) – SPECIAL CASES

Roadway Gaps / Dog Legs

Sta. 0+0

Sta. 1+50

Sta. 2+50

Sta. 5+0

15

LINEAR REFERENCE SYSTEM (LRS) – SPECIAL CASES

Roadway Realignment / By-Pass

Sta. 0+0

Sta. 1+50

Sta. 1+75

Sta. 3+0

Sta. 1+100

Sta. 1+150

Sta. 1+200 Back = 1+75 Ahead

16

OAHU HPMS WITH NON-NHS & LOCAL ROUTES 17

INVENTORY DATA 18

INVENTORY DATA: PAVEMENT STRUCTURE HISTORY 19

INVENTORY DATA: PAVEMENT STRUCTURE HISTORY 20

HI DOT PAVEMENT THICKNESS 21

22

HI DOT PAVEMENT THICKNESS

MAINTENANCE INVENTORY DATA 23

DERIVED DATA – MEDIAN WIDTH 24

INVENTORY DATA PROCESS: TRAVEL MONITORING

25

INVENTORY DATA PROCESS: TRAVEL MONITORING

Travel Monitoring Plan

Annual Data Collection

Station AADT, K, D, Peak

HPMS Section AADT, K, D, Peak

TMG Requirements

Data Provider

26

TRAVEL MONITORING: INVENTORY DATA SAMPLING

Functional Class

Urban Code

Facility Type

Through Lanes

AADT

HPMS TABLE OF POTENTIAL SAMPLES (TOPS)

27

HPMS TRAVEL INVENTORY ITEMS 28

DERIVED HPMS TRAVEL ITEMS 29

ANNUAL TRAVEL MONITORING DERIVED DATA 30

RAW AND DERIVED DATA 31

RAW AND DERIVED DATA 32

TRAVEL MONITORING QA/QC CHECKS

1. Historical 24hr volume count consistency

2. Historical 24hr directional count consistency

3. Compare with nearby Permanent Station

4. Check Historical AM/PM Peak by Direction

33

AADT Obtained from

Continuous Count Stations – (24/7)

34

K-factor - The design hour volume (30 th largest hourly volume for a given calendar year) as a percentage of AADT. Computed using continuous count sites by ranking the observed hourly volumes.

Directional Factor (D) - The percent of design hour volume (30th largest hourly volume for a given calendar year) flowing in the higher volume direction.

35

MONTHLY FACTORS

ISLAND STATION Yr Oahu C7L 2015

Month ADT Factor January 223716 1.03 February 230187 1.00 March 231319 1.00 April 233336 0.99 May 227484 1.02 June 231540 1.00 July 236930 0.98 August 229611 1.01 September 230538 1.00 October 232752 0.99 November 227738 1.01 December 241228 0.96 AADT 231065

0.92

0.94

0.96

0.98

1

1.02

1.04

210,000

215,000

220,000

225,000

230,000

235,000

240,000

245,000

ADT Factor

36

AADT

Sample (2015 data): Station – C7L Number of Counts – 315 AADV = 231365 AADT = 231065 DHV (30th Highest Hrly. Volume) = 11948 K = DHV/AADT * 100 = 5% Dmax = 53.6%

37

VEHICLE CLASSIFICATION

Source TMG 2016 – pg. 3-37

38

AADT CLASS FACTORS

Single Station Class Factors for Station C7L

39

Class MC PC LTrk Bus SU CU TotalVol MADTvc (Jan) 201 142309 62643 875 12028 1205 219261 AADTVC 207 140617 65987 968 17534 1230 226544 Class Factors 1.03 0.99 1.05 1.11 1.46 1.02

CLASSIFICATION DATA COMPUTATION (TO BE UPDATED WITH REAL DATA)

Class MC PC LTrk Bus SU CU Sunday 1.15 1.24 1.37 3.04 1.34 5.21 Monday 0.97 1.00 0.98 0.87 0.98 0.80 Tuesday 0.94 0.96 0.94 0.80 0.93 0.78

Wednesday 0.93 0.95 0.94 0.81 0.94 0.76 Thursday 0.93 0.96 0.94 0.82 0.96 0.78

Friday 0.94 0.92 0.89 0.79 0.91 0.80 Saturday 1.22 1.03 1.07 1.82 1.03 2.33

Weekday Class Factors for Station C7L

40

AADT Short Duration Counts – Factored using Continuous Count Stations

COUNTDATE CYCLES PC LT_TRKS BUS SU CU 1/22/2015 (Thursday) 220 18627 4662 88 120 104 1/23/2015 (Friday) 206 19343 4892 78 131 131 Factor Jan. 1.03 0.99 1.05 1.11 1.46 1.02 Thursday Factor 0.93 0.96 0.94 0.82 0.96 0.78 Friday Factor 0.94 0.92 0.89 0.79 0.91 0.80 Adjusted Data 210 18318 4514 74 119 83

199 18234 4503 63 123 108 Average 205 18276 4509 69 121 95

41

HPMS LINK TRAVEL DATA ASSIGNMENT

42

TRAVEL MONITORING: THE IDEAL ENVIRONMENT 43

TRAVEL MONITORING: THE IDEAL ENVIRONMENT 24/7 continuous counters

44

TRAVEL MONITORING: AN EVEN BETTER ENVIRONMENT

Autonomous Self-Reporting Vehicles

45

TRAVEL MONITORING: THE REAL ENVIRONMENT 24/7 continuous counters Short Term Counters

?

?

?

? ?

? No Counters

46

Count coverage (continuous vs. short-term counters)

Equipment malfunction

Vehicle classification

Seasonal variations

Daily variations

SOURCES OF ERROR 47

Station – Link Assignments

Spatial Proximity with TOPS attribute filters (functional class, urban code, etc.)

Cluster analysis

HPMS LINK TRAVEL DATA ASSIGNMENT 48

Homogeneous traffic volume (± 10%)

For controlled access roadways (e.g. interstate system), in-between interchanges is appropriate.

Urban vs. rural boundaries

Low volume rural roadways

STATION – LINK ASSIGNMENTS DEFINING ROADWAY SEGMENTS

[Ref. FHWA Traffic Monitoring Guide 2016]

49

DEFINING ROADWAY SEGMENTS 24/7 continuous counters Short Term Counters

?

?

?

? ?

? No Counters

Segment Boundaries

50

DEFINING ROADWAY SEGMENTS

? ?

?

?

? ?

?

51

CONSIDERATIONS FOR ASSIGNING DATA FROM MONITORED TO UNMONITORED SEGMENTS

Functional Classification

Urban vs. Rural boundaries

Proximity

52

FUNCTIONAL CLASSIFICATION

All Roads

Arterial

Principal

Full Control

Interstate Other

Freeways & Expressways

Partial/ Uncontrolled

Other Principal Arterial

Minor

Non-Arterial

Collector

Major Minor

Local

[Ref. FHWA and CDM Smith]

53

FUNCTIONAL CLASSIFICATION

Urban and Rural

1. Interstate

2. Principal Other Freeways and Expressways

3. Principal Other Arterial

4. Minor Arterial

5. Major Collector

6. Minor Collector

7. Local

[Ref. FHWA]

54

ESTIMATION PROCEDURE FOR UNMONITORED LINKS

Functional Class

Interstate, Principal Other

Freeways & Expressways,

Arterial

Minor Arterial, Major Collector, Minor Collector,

Local

Crosses Urban/Rural Boundary?

Walk each route and assign average AADTs

from closest links with counts

Identify routes in each

urban/rural boundary by

FC

For each link in each route, find closest

route with similar FC and assign

55

WHAT IFS …

What if no functional class within an urban/rural boundary was counted? Use estimates from other urban/rural boundaries

with similar characteristics e.g. population

What if no functional class within the entire state with similar urban/rural characteristics is found? Signifies limitation in statewide data collection

program Explore concept of “geographically closed system”

56

“A GEOGRAPHICALLY CLOSED SYSTEM” 57

https://en.wikipedia.org/wiki/Closed_system#/media/File:Diagram_Systems.png

“A GEOGRAPHICALLY CLOSED SYSTEM” ASSUMPTIONS IN TRAFFIC FLOW ESTIMATION

58

Minor Arterials, Major and Minor Collectors, and Local Roadways account for majority of traffic flow in an urban/rural boundary

Can AADT estimates be derived based on the hierarchical relationship between roadway networks?

𝐴𝐴𝐴𝐴𝐴𝐴𝑇𝑇𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 = ∑ 𝐴𝐴𝐴𝐴𝐴𝐴𝑇𝑇𝑓𝑓𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙_𝑓𝑓𝑙𝑙𝑙𝑙𝑙𝑙𝑓𝑓_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑙𝑙𝑓𝑓 ?𝑛𝑛𝑓𝑓= 0

OAHU ROADWAYS AND TRAFFIC COUNTS 59

OAHU ROADWAYS AND TRAFFIC COUNTS 60

OAHU ROADWAYS AND TRAFFIC COUNTS 61

Continuous Counter

OAHU ROADWAYS AND TRAFFIC COUNTS 62

Continuous Counter

2016 Short Term Counts

SPATIAL MODELING FOR HIGHWAY PERFORMANCE MONITORING SYSTEM DATA – PART 2 ITEMS

63

Step-by-step network AADT estimation process using spatial modeling

Validation and reporting

Ramp balancing

Probe data opportunities and challenges

REFERENCES

FHWA HPMS Website and Field Manual

2016 Traffic Monitoring Guide

Highway Functional Classification Concepts, Criteria and Procedures, 2013 Edition

Continuous Process Improvement , 2001, FHWA

64

CONTACT INFORMATION

Maaza Christos Mekuria, PhD, PE, PTOE Hawaii Department of Transportation maaza.c.mekuria@hawaii.gov Dan P. Seedah, PhD Asst. Research Scientist, Texas A&M Transportation Institute d-seedah@tti.tamu.edu

65

Today’s Participants

• Jennifer Campbell, Oregon Department of Transportation, jennifer.k.campbell@odot.state.or.us

• Maaza Mekuria, Hawaii Department of Transportation, maaza.c.mekuria@hawaii.gov

• Daniel Seedah, Texas A&M Transportation Institute, d-seedah@tti.tamu.edu

Panelists Presentations

http://onlinepubs.trb.org/onlinepubs/webinars/180227.pdf

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