1 / 26

Congestion and Time-Of-Day Forecast Outcomes of an Activity-Based Model

Congestion and Time-Of-Day Forecast Outcomes of an Activity-Based Model . Congestion and Time-Of-Day Forecast Outcomes of an Activity-Based Model. Erik Sabina – Regional Modeling Manager Suzanne Childress – Senior Modeler Transportation Research Board Planning Applications Conference

huela
Download Presentation

Congestion and Time-Of-Day Forecast Outcomes of an Activity-Based Model

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Congestion and Time-Of-Day Forecast Outcomes of an Activity-Based Model

  2. Congestion and Time-Of-Day Forecast Outcomes of an Activity-Based Model Erik Sabina – Regional Modeling Manager Suzanne Childress – Senior Modeler Transportation Research Board Planning Applications Conference Reno, Nevada, May 10th, 2011

  3. A Bit About Denver • 2010 • Population 2.9m • Employment 1.6m • 2035 • Population 4.5m • Employment 2.6m • Planning Goals • Urban Centers • Urban Growth Boundary • New regional light rail • TODs

  4. The Question: why this outcome?

  5. The answer: probably two threads • Expected time of day effects of the activity-based model. • Interaction effects between disaggregate demand-side model and aggregate supply side (static equilibrium)

  6. The new FOCUS Model Population Synthesis Work-based Sub-tour Generation Workplace Location Tour Destination School Location Tour Mode Auto Availability Tour Time of Day Daily Activity Pattern Intermediate Stop Generation Exact Number of Tours Intermediate Stop Location Work Tour Destination Type Trip Mode Intermediate Stop Departure Time

  7. The new FOCUS Model Population Synthesis Work-based Sub-tour Generation worker? Workplace Location Tour Destination School Location Tour Mode Auto Availability Tour Time of Day Daily Activity Pattern Intermediate Stop Generation Exact Number of Tours Intermediate Stop Location Work Tour Destination Type Trip Mode Intermediate Stop Departure Time

  8. The new FOCUS Model Population Synthesis Work-based Sub-tour Generation worker? Workplace Location Tour Destination School Location Tour Mode Auto Availability Tour Time of Day Daily Activity Pattern Intermediate Stop Generation Exact Number of Tours Intermediate Stop Location Work Tour Destination Type Trip Mode Intermediate Stop Departure Time

  9. Key variables: accessibility Population Synthesis Work-based Sub-tour Generation Workplace Location Tour Destination School Location Tour Mode Auto Availability Tour Time of Day Daily Activity Pattern Intermediate Stop Generation Exact Number of Tours Intermediate Stop Location Work Tour Destination Type Trip Mode Intermediate Stop Departure Time

  10. Key variables: age Population Synthesis Work-based Sub-tour Generation Workplace Location Tour Destination School Location Tour Mode Auto Availability Tour Time of Day Daily Activity Pattern Intermediate Stop Generation Exact Number of Tours Intermediate Stop Location Work Tour Destination Type Trip Mode Intermediate Stop Departure Time

  11. Our Trip-Based Model i. Network Processing ii. Area Type 1. Trip Generation i. Highway/Transit Skims 2. Trip Distribution 3. Mode Choice i. Parking Cost ii. Time-of-Day 4. Highway/Transit Assignment

  12. Our old time of day “model”

  13. Basic Results: 2010 and 2035 trips by time of day

  14. Basic Results: 2035 – 2010time of day by purpose

  15. Drivers of those results 1

  16. Driver of Results 2

  17. Validation Challenges:peak spreading?

  18. Return to the question: why this outcome?

  19. Calibration Outcome: trip time of day Trip time-of-day

  20. Calibration Outcome: trip time of day versus VHT Trip time-of-day VHT

  21. Possible Remedies • Interfere with the calibration outcomes of the time-of-day choice models – REALLY BAD IDEA! • Adjust “WriteTripsToTransCAD” module • Module that writes from database into TransCAD matrices for assignment • Outbound half-tour trip times are arrival times – bias backward in time. • Return-bound half-tour trip times are departure times – bias forward in time. • Key point – distribution INSIDE each hour. • Proper remedy – DTA?

  22. Modify WriteTripsToTransCAD

  23. Conclusions • Interaction issues between static assignment and ABMs can significantly affect congestion outcomes. • ABMs do show “peak spreading” in forecast years (at this point, not appearing to be huge.) • Demographic and accessibility variables appear key. • Calculate arc elasticities via scenario runs to better evaluate sensitivities. • Validation will be a challenge in any case.

  24. Contact Information • Suzanne Childress – schildress@drcog.org • Erik Sabina – esabina@drcog.org • 303-455-1000

  25. Influence of components Population Synthesis Work-based Sub-tour Generation Workplace Location Tour Destination School Location Tour Mode Auto Availability Tour Time of Day Daily Activity Pattern Intermediate Stop Generation Exact Number of Tours Intermediate Stop Location Work Tour Destination Type Trip Mode Intermediate Stop Departure Time

  26. Calibration Outcome: trip time of day versus VHT Trip time-of-day

More Related