How Reliable is Your Road Network: A Comprehensive Approach to Performance Evaluation

Transportation Education SeriesSeptember 19, 2012

Overview

• Welcome and Introductions• Key Concepts and Definitions• Research and State of Practice• Application of Reliability Concepts• Q &A

Why is Reliability Important?

1) Our roadway networks are more frequently operating in a near or above capacity state

2) Uncertainties in travel time adversely impact us in multiple ways

3) We don’t have a way to measure the benefit of many of our strategies and investments

Source: http://ops.fhwa.dot.gov/publications/tt_reliability/TTR_Report.htm

How is a Reliability-focused Analysis Different?

Traditional Analysis Approach

Reliability‐Focused Analysis Approach

Spatial Element Analyzed Point Path/Network

Temporal Analysis Period Single Day Multiple days

Type of Congestion Accounted For Recurring All

Demand Effects Considered? No Yes

Statistical Reporting Average Full Distribution

A Shift in Thinking

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PAST FUTURE

Increase CapacityReduce Travel Time

Increase Capacity UtilizationMinimize DisruptionsImprove Predictability

Paradigms are shifting

• Old Approach (Capacity-oriented)

• Network build-out and expansion

• Secure funding environment

• Traditional performance metrics

• New Approach (Reliability-oriented)

• How best to manage the system we have

• Financial, environmental and public perception problems

• Improvements that affect reliability more than capacity

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SHRP 2 Reliability Program Goal• The central goal of the SHRP 2 Reliability focus area is

to reduce non-recurring congestion and improve travel time reliability through:• Incident reduction• Management• Response • Mitigation

• Theme areas within the Reliability Program include:• Data, Metrics, Analysis, and Decision Support• Institutional Change, Human Behavior, and Resource Needs• Incorporating Reliability in Planning, Programming, and Design• Fostering Innovation to Improve Travel Time Reliability

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SHRP 2 Reliability Projects

• About 20 SHRP 2 Projects, 10 active projects

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http://www.trb.org/StrategicHighwayResearchProgram2SHRP2/PublicationsSHRP2.aspx

SHRP 2 L02 Establishing Monitoring Programs for Mobility and Travel Time Reliability

SHRP 2 L04 Incorporating Reliability Performance Measures in Operations and Planning Modeling Tools

SHRP 2 L05 Incorporating Reliability Performance Measures into the Transportation Planning and Programming Processes

SHRP 2 L06 Institutional Architectures to Advance Operational Strategies

SHRP 2 L07 Evaluation of Cost‐Effectiveness of Highway Design Features

SHRP 2 L08 Incorporation of Travel Time Reliability into the Highway Capacity Manual

SHRP 2 L13ADesign and Implement a System for Archiving and Disseminating Data from SHRP 2 Reliabilities and Related Studies/ Assistance to Contractors to Archive their Data for Reliability Projects

SHRP 2 L14 Traveler Information and Travel Time Reliability

SHRP 2 L15 Innovative IDEA Projects

SHRP 2 L17 A Framework for Improving Travel Time Reliability

Concept of Incorporating Reliability into HCM

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IV. Research and State-of-Practice

• SHRP2 Program• Jim Bonneson’s presentation

SHRP2 LO8

• (Insert Jim’s presentation)

V. Applications of Concepts

• Methodology and Tool• Research NCHRP 3-97: Traffic Signal Analysis With Varying

Demands and Capacities• Example: El Camino Real

• Data and I-680 Application

NCHRP 3-97: Project Objectives

• To revise the signal analysis methodologies contained in the year 2010 edition of the Highway Capacity Manual (HCM 2010) so as…..

• to allow determination of the effectiveness of traffic signal operations and timing plans given variability in demands and capacities.

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Research Products

• Information on demand and capacity variance for signal systems (and how to collect it locally)

• HCM style method for predicting the robustness of signal timing plans under varying demand and capacity conditions

• An implementation plan that provides specific recommendations on how the methodologies might be included in a post-2010 HCM edition.

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Methodology Objectives

• Should work with typical signal analysis tools• HCM, Synchro, Transyt, etc.

• Should not require excessive data

• Should provide measures of robustness

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The Analysis Methodology

• Select signal analysis tool• HCM, microsimulation, other

• Generate 9 d/s ratio scenarios for testing• Estimate probability for each scenario

• Apply tool to scenarios

• Tally and sum results based on probabilities

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4. Performance Measure Calculator

‐ Annual Mean Delay/Vehicle‐ Annual Mean Stops/Vehicle‐ Prob. of Queue Overflow‐ Probability of Breakdown

‐ One week directional counts‐ AM/Midday/PM turn counts‐ Geometry, Saturation flows‐ Signal system control type‐ Signal timing parameters

1. Conventional Signal Optimization Data

‐ Scenario selection‐ Scenario probabilities‐ Scenario input files

2. Scenario Generator

‐ Highway Capacity Manual, ‐TRANSYT, SYNCHRO, etc.

ScenariosResults

ScenariosInput Files

3. Conventional SignalEvaluation Tool

NCHRP 3-97 Methodology

Scenario Generator

• Construct D/S scenarios from demand profile and capacity profile

• Presumption: can predict travel time distribution from D/S scenarios.

• Demand Profiles – 10 in NCHRP 3-97 Library• Capacity Profiles – 3 in NCHRP 3-97 Library

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Demand Profiles

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Demand Profiles (Type 1 example)

200

100

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1,000

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

1%

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95%

99%

Demand Profiles (Type 1 example)

210

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1,000

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

1%

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95%

99%

Cumulative Demand Profile

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Capacity (Saturation) Profiles

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Combined D/S Profile

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Ratio of Two Independent Normal Distributions

Cumulative D/S Ratio Distribution

250 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

D/S Ratio

Per

cent

ile

(Volume/AADT)*8000 + Warm Climate Saturation FlowScenario

Cumulative D/S Ratio Distribution

260 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

D/S Ratio

Per

cent

ile

(Volume/AADT)*8000 + Warm Climate Saturation FlowScenario

Cumulative D/S Ratio Distribution

270 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

D/S Ratio

Per

cent

ile

(Volume/AADT)*8000 + Warm Climate Saturation FlowScenario

CountDay

Cumulative D/S Ratio Distribution

280 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

D/S Ratio

Per

cent

ile

(Volume/AADT)*8000 + Warm Climate Saturation FlowScenario

CountDay

The Key

• Pivot off of Critical Thru D/S Approach• Preserve demand relationship of critical approach to all

others

• Is this an adequate basis for predicting real world variation in timing plan performance under varying capacities and demands?

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Performance Measurement

• Robustness: • “The ability of the system to continue to perform satisfactorily

under exceptional circumstances.”• Performance Measures:

• Average Annual Delay per vehicle• Average Annual Stops per vehicle• Probability of Breakdown #1 = Queues overflowing blocks

(QSR > 1)• Probability of Breakdown #2 = Demand > Capacity

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Example Urban Street Application

• El Camino Real, San Mateo, CA

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Robustness of Current Timing Plan

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Scenario  Probability 

Distance Travelled 

Travel Time  Delay  Stops  D/C > 1.00  QSR > 1.00 

Average Speed 

(veh‐mi/hr)  (veh‐hr/hr)  (sec/veh) (stops/veh) (Links) (Links) (mph)5th Percentile  7.50%  879 74 15.5 3 0 1 11.9

10th Percentile  5.00%  984 88 16.9 4 0 1 11.2

15th Percentile  7.50%  1,053 99 18.1 5 0 1 10.6

25th Percentile  17.50%  1,154 120 20.4 7 0 3 9.7

50th Percentile  25.00%  1,339 176 27.5 13 0 6 7.6

75th Percentile  17.50%  1,519 302 44.4 32 4 8 5.0

85th Percentile  7.50%  1,614 421 60.1 45 5 8 3.8

90th Percentile  5.00%  1,678 516 71.8 52 6 8 3.3

95th Percentile  7.50%  1,772 673 90.2 58 7 8 2.6

Average     1,334  243 36.4 21 2 5 7.4

Prob (QSR > 1) =  100.00% 

Prob (D/C > 1) =  43.80% 

Robustness of Higher Cap Plan

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Scenario  Probability 

Distance Travelled 

Travel Time  Delay  Stops  D/C > 1.00 

QSR > 1.00 

Average Speed 

(veh‐mi/hr)  (veh‐hr/hr)  (sec/veh) (stops/veh) (Links) (Links) (mph)5th Percentile  7.50%  865  72  15.2  3  0  0  12.0

10th Percentile  5.00%  969  86  16.7  4  0  0  11.3

15th Percentile  7.50%  1,039  97  18.0  5  0  0  10.7

25th Percentile  17.50%  1,141  118  20.5  7  0  3  9.6

50th Percentile  25.00%  1,328  177  27.9  13  0  6  7.5

75th Percentile  17.50%  1,512  292  43.1  29  3  7  5.2

85th Percentile  7.50%  1,610  387  55.0  37  3  7  4.2

90th Percentile  5.00%  1,675  462  63.9  39  4  7  3.6

95th Percentile  7.50%  1,772  596  79.1  47  6  8  3.0

Average      1,325  230  35.0  19  1  5  7.5

Prob (QSR > 1) =  81.70% 

Prob (D/C > 1) =  41.70% 

Delta (New-Original)

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Scenario Prob‐ability 

Distance Travelled  Travel Time  Delay  Stops 

D/C > 1.00 

QSR > 1.00 

Average Speed 

(veh‐mi/hr)  (veh‐hr/hr)  (sec/veh) (stops/veh) (Links) (Links) (mph)5th Percentile  7.50%  ‐2% ‐3% ‐2% 0% #DIV/0! ‐100% 1%

10th Percentile  5.00%  ‐2% ‐2% ‐1% 0% #DIV/0! ‐100% 1%

15th Percentile  7.50%  ‐1% ‐2% ‐1% 0% #DIV/0! ‐100% 1%

25th Percentile  17.50%  ‐1% ‐2% 0% 0% #DIV/0! 0% ‐1%

50th Percentile  25.00%  ‐1% 1% 1% 0% #DIV/0! 0% ‐1%

75th Percentile  17.50%  0% ‐3% ‐3% ‐9% ‐25% ‐13% 4%

85th Percentile  7.50%  0% ‐8% ‐8% ‐18% ‐40% ‐13% 11%

90th Percentile  5.00%  0% ‐10% ‐11% ‐25% ‐33% ‐13% 9%

95th Percentile  7.50%  0% ‐11% ‐12% ‐19% ‐14% 0% 15%

Average     ‐1% ‐5% ‐3% ‐10% ‐26% ‐10% 1%

Prob (QSR > 1) =  ‐18% 

Prob (D/C > 1) =  ‐5% 

Software

• Conceptual Framework – works with any signal analysis software (Synchro, HCM, T7F)

• Example Processor – Works with Transyt 7F

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Example Software Output

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Possible Applications

• Evaluating annual benefits of sub-optimal (looser) plans that have more spare capacity to handle demand surges and incidents.

• Evaluating the benefits of multiple coordination plans versus fewer plans.

• Evaluating annual benefits of traffic responsive (SCAT) control compared to traditional coordinated- actuated control.

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For More Information

• NCHRP 3-97 Research Team• Rick Dowling, Kittelson & Associates• Alex Skabardonis, U.C. Berkeley• Richard Margiotta, Cambridge Systematics• Rob Hranac, Berkeley Transportation Systems• Alastair Maxwell, TRL (Transport Research Lab)• Moses Wilson, Wiltec

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Example Application

Travel time• Freeway

• Example: I-680 using MTC 511.org data

Travel Time Analysis I-680

Study Corridor: I 680 SB between SR 84 to SR 237Control Corridor: I-680 NB between Alcosta and Livorna

Travel Time Data Sources

• MTC 511.org Transponder Travel Time Data• Travel Time for months of September and October 2008.

• Data averaged for each minute• Data was averaged for Tuesdays, Wednesdays and

Thursdays for a total of 24 days.• Mean was computed for each hour.• For each hour, 24 X 60=1,440 computations

Travel Time Statistics

• Reliability was measured by the amount of variation of travel times.

• The statistics used for travel time:• Mean Travel Time by time of day: used as a measure of central

tendency for the data.• Standard Deviation of travel time by time of day: Information on

variability of travel time.• Coefficient of Variation of travel time by time of day

• Ratio of Standard Deviation to the mean.• Standard Deviation in context of the mean of the data

I-680 SB Travel Times

I-680 SB Standard Deviation and Coefficient of Variation Plots

Variability increases and reliability decreases during the AM peak hour

I-680 SB Mean Travel Time

I-680 NB Travel Times

I-680 NB Standard Deviation and Coefficient of Variation Plots

Variability increases and reliability decreases during the AM and PM peak hours

I-680 NB Mean Travel Time

Questions?

• Jim Bonneson• jbonneson@kittelson.com• 979-319-1886

• Rick Dowling• rdowling@kittelson.com• 510-839-1742 ext. 120

• Pratyush Bhatia• pbhatia@kittelson.com• 510-839-1742 ext. 116