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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
5
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
6
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
7
SHRP 2 Reliability Projects
• About 20 SHRP 2 Projects, 10 active projects
8
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
9
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.
13
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.
14
Methodology Objectives
• Should work with typical signal analysis tools• HCM, Synchro, Transyt, etc.
• Should not require excessive data
• Should provide measures of robustness
15
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
16
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
18
Demand Profiles
19
Demand Profiles (Type 1 example)
200
100
200
300
400
500
600
700
800
900
1,000
0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00
1%
5%
10%
15%
25%
50%
75%
85%
90%
95%
99%
Demand Profiles (Type 1 example)
210
100
200
300
400
500
600
700
800
900
1,000
0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00
1%
5%
10%
15%
25%
50%
75%
85%
90%
95%
99%
Cumulative Demand Profile
22
Capacity (Saturation) Profiles
23
Combined D/S Profile
24
⎥⎥⎦
⎤
⎢⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛+−+⎥
⎦
⎤⎢⎣
⎡−⎟⎟
⎠
⎞⎜⎜⎝
⎛Φ= 2
2
2
2
2223 21exp
*)(11
)()(2
21
)()(*)()(
y
y
x
x
yxyx
z zazazb
zazczbzp
σμ
σμ
σπσσπσ
22
2
11)(yx
zzaσσ
+=22)(y
y
x
x zzbσμ
σμ
+=
⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎥⎥⎦
⎤
⎢⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛+−= 2
2
2
2
2
2
)()(
21exp)(
y
y
x
x
zazb
zcσμ
σμ
dzezz
∫∞−
−=Φ
2
21
21)(
μ
π
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?
29
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
30
Example Urban Street Application
• El Camino Real, San Mateo, CA
31
Robustness of Current Timing Plan
32
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
33
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)
34
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
35
Example Software Output
36
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.
37
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
38
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• [email protected]• 979-319-1886
• Rick Dowling• [email protected]• 510-839-1742 ext. 120
• Pratyush Bhatia• [email protected]• 510-839-1742 ext. 116