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Lili (Leo) Luo, P.E., ITS & Safety Engineer

Can Multi-Resolution Dynamic Traffic Assignment live up to the Expectation of Reliable Analysis of Incident Management Strategies. Lili (Leo) Luo, P.E., ITS & Safety Engineer Sarath Joshua, P.E., Ph.D., ITS & Safety Program Manager. Basic need for evaluation of ITS operational strategies.

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Lili (Leo) Luo, P.E., ITS & Safety Engineer

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  1. Can Multi-Resolution Dynamic Traffic Assignment live up to the Expectation of Reliable Analysis of Incident Management Strategies Lili (Leo) Luo, P.E., ITS & Safety Engineer Sarath Joshua, P.E., Ph.D., ITS & Safety Program Manager

  2. Basic need for evaluation of ITS operational strategies • Low cost • Efficient • Reliable • Capable of evaluating area wide impact

  3. How did we get started? • Neither the static Macroscopic Travel Demand Model nor the Microscopic simulation model met our needs • Decision to test the emerging Mesoscopic simulation-based DTA model through a Case Study

  4. The Case Study Approach • Convert the 4-step TDM model into DTA (DynusT) model • Study a major freeway traffic incident that occurred on I-10 during morning peak period • Calibrate the subarea baseline (Normal) model • Perform scenario analysis • Convert stretch of interest into microscopic simulation model for visualization • The modeling effort was conducted in house at MAG

  5. Purpose of the case study • Test if the DTA model can be used as an operations planning tool to evaluate strategies • Obtain hands-on experience on DTA model from model calibration to scenario analysis • Experiment with the concept of Multi-Resolution modeling method

  6. MAG Regional DTA model • 2006 TAZs • Over 10,000 SQ Miles • 20508 Links • 9893 Nodes • 2364 Signalized intersections • 2.78M Trips during 5 hours of morning peak

  7. Impacted Area of a major Incident

  8. Model Calibration—Count Calibration • 87 sensors • Collected over a three year time frame

  9. Model Calibration – AM Peak Period Travel Time V.S. GPS Run

  10. Space-Time Diagram showing bottleneck locations Loop202 Red Mountain WB 5:00 10:00 Loop 101 NB US 60 WB 24th St Airport Rural L101 5:00 10:00 McKellips Loop 202 Santan WB I-10 Priest Dr. Mill Ave Rural Rd Loop 101 US60 5:00 10:00 Fry I-10 Kyren 5:00 10:00 L101

  11. Time-Dependent Travel Time By Freeway

  12. Real-life Incident • February 4, 2010 • Location: Salt River Bridge on I-10 Westbound • Time 6:20AM • Duration: 3 hour 20 minutes • Severity: Three lanes blocked all the time, I-10 completely shut down three times.

  13. Incident Scenario • Scenario 0 — Baseline (Normal Condition) • Scenario 1— With ITS Infrastructure (DMS and Ramp Metering) turned on • Scenario 2 — Without ITS Infrastructure

  14. Traveler Information and Congestion Response • Pre-trip and In-route information • DMS congestion warning • Driver congestion response behavior

  15. Scenario 1 With ITS Infrastructure

  16. Scenario 1: With ITS infrastructure • Tempe to Downtown Phoenix Travel Time Downtown Phoenix South Tempe & Northwest Chandler

  17. Affected Routes

  18. 3 Mile Upstream of Crash Site on I-10

  19. Samples of impacted vehicle

  20. Another Vehicle

  21. Scenario 2 (No ITS Infrastructure) • Tempe to Downtown Phoenix Travel Time Downtown Phoenix South Tempe & Northwest Chandler

  22. Scenario 2 (No ITS Infrastructure)

  23. Baseline VS incident with ITS VS Incident without ITS

  24. Scenario 2 (No ITS Infrastructure)

  25. Vehicles under No ITS scenario

  26. Another Vehicle

  27. Summary

  28. Meso-Micro conversion • Selected area subarea cut for VISSIM micro simulation using DynusT-VISSIM converter • All time-dependent routes and flows are converted • A little more network clean up • More detail timing and crash setup

  29. VISSIM Microscopic Simulation

  30. Conclusions • Mesoscopic Simulation-based DTA model DynusT: • Capable of demonstrating the status of our freeway system operation in a capacity restraint and time-dependent manner. • Able to match field observed data following relatively simple procedure for model calibration.

  31. Conclusions • Logical scenario analysis outcomes • Demonstrate the benefit of traveler information/ITS during an incident with region wide impact • Reasonable cost and effort • Combined with Macro and Micro models for Multi-Resolution Modeling to answer complicated questions • Appear useful for operations planning

  32. Lessons Learnt • Leverage data collection efforts with Travel Demand Model • More sensitive to network and data errors—Very Important!!! • Could extract more useful information from the model • Start with the entire regional model

  33. Questions Email: lluo@azmag.gov Tel: 602-452-5072

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