Dynamic Traffic Modeling


Published on

As part of the 2014 CTR Symposium, Dr. Stephen Boyles discussed the fundamentals of dynamic traffic modeling, and the field's frontiers moving forward.

Published in: Engineering, Business
1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Dynamic Traffic Modeling

  1. 1. Dynamic Traffic Modeling: Applications and Frontiers Stephen Boyles Assistant Professor Civil, Architectural & Environmental Engineering The University of Texas at Austin April 23, 2014 CTR Symposium Austin, TX
  2. 2. A question to start… Construction work will restrict capacity on a major route for an extended period of time. Should traffic signals be retimed?
  3. 3. IT DEPENDS!!!
  4. 4. Some relevant factors to consider: 1. How long is the closure? 2. What is the capacity reduction? 3. What alternate routes exist? 4. What signals can you control? 5. What about impacts on other traffic? 6. How much data do you have available? 7. How much information will travelers have? and so on…
  5. 5. Many of these factors can be grouped into two categories: “Supply” side Lane closures Speed limit changes Changes in signal timing “Demand” side Diversion rates Traveler information Sensitivity to delay
  6. 6. How large should the study area be? On the one hand, focusing on a small area or corridor allows you to use more realistic simulators. On the other hand, projects can have impacts far beyond the immediate area.
  7. 7. Microscopic models simulate a small area. Mesoscopic models strike a balance. Macroscopic models simulate a large area. Realism Supply-side Demand-side Dynamic traffic assignment
  8. 8. Talk outline: 1. Brief background on dynamic traffic assignment 2. Applications of DTA 3. Current research frontiers
  10. 10. Supply and demand are dependent on each other. In traffic assignment models, we find a mutually consistent solution in which drivers’ decisions are consistent with the traffic conditions they expect. (Often called equilibrium.) Supply: Traffic flow model Demand: Route choice model Path choices Departure times Congestion Travel times
  11. 11. An example The top route is long, but uncongested. The bottom route is shorter, but has limited capacity.
  12. 12. An example After drivers gain experience, some will choose the top route, and some will choose the bottom, in a way that the travel times are roughly equal.
  13. 13. An example When capacity is added to the bottom route, drivers will divert from the top route to the bottom. This diversion would continue until the travel times were equal again… there is no improvement in travel times.
  14. 14. What makes DTA “dynamic”? The traffic flow model is simpler than CORSIM or VISSIM, but still tracks queue buildup and dissipation, signal delays, etc. and can be applied on a regional scale.
  16. 16. Work zone impacts • Traffic Control Plans (TCPs) are used to coordinate phasing of roadway construction projects • DTA models have been demonstrated to effectively identify impacts due to construction projects/TCP scenarios – Provide traffic control – Identify roadway realignments, lane closures, and detours
  17. 17. Subnetworks ?
  18. 18. Scenarios • A combination of network modifications varying with respect to modification scope were used to assess 54 impact scenarios • Three locations: Guadalupe Street, 15th Street, and 7th Street • Three different sized subnetworks within the downtown Austin network: Connected order of 5, 7, and 9 • Five Real-world Scenarios
  19. 19. Subnetwork Size Evaluation: Recommendations
  20. 20. Emissions modeling with MOVES Dynamic models provide enough detail for emissions models, while still feasible to run on large, regional scales.
  21. 21. FRONTIERS
  22. 22. Making model calibration easier
  23. 23. Making model calibration easier
  24. 24. Integration with activity-based models Shopping Kid’s School Home Work Restaurant 7:15 AM 7:30 AM 7:35 AM 8:00 AM 12:30 PM 12:35 PM 1:00 PM 1:05PM5:00 PM 5:30 PM 6:00 PM 6:30 PM Drive Drive Drive Drive Walk Walk
  25. 25. Autonomous vehicles
  26. 26. Autonomous vehicles
  27. 27. Autonomous vehicles
  28. 28. Conclusions • DTA can integrate human behavior into traffic modeling.
  29. 29. Conclusions • DTA can integrate human behavior into traffic modeling. • It has applications in many areas requiring realistic traffic estimates over large regions.
  30. 30. Conclusions • DTA can integrate human behavior into traffic modeling. • It has applications in many areas requiring realistic traffic estimates over large regions. • The future is bright in traffic assignment!