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Estimating the Traffic Flow Impact of Pedestrians With Limited Data

Estimating the Traffic Flow Impact of Pedestrians With Limited Data. Daniel Tischler , Elizabeth Sall , Lisa Zorn & Jennifer Ziebarth. 14 th TRB Planning Applications Conference May 6 th , 2013. Making the Case. I have problems. I’m trying to model San Francisco traffic conditions, but

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Estimating the Traffic Flow Impact of Pedestrians With Limited Data

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  1. Estimating the Traffic Flow Impact of Pedestrians With Limited Data Daniel Tischler, Elizabeth Sall, Lisa Zorn & Jennifer Ziebarth SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 14th TRB Planning Applications Conference May 6th, 2013

  2. Making the Case • I have problems. • I’m trying to model San Francisco traffic conditions, but • Pedestrians affect vehicle capacity • My dynamic traffic assignment model is needy • Data is scarce SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  3. Network capacity Road capacity is a key input in traffic assignment We assign capacities to roads by facility classification schemes SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  4. Network capacity In our DTA model, signal timing becomes the primary determinant of capacity SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  5. Pedestrians interact with vehicles Lots of pedestrians crossing the street prevent cars from turning SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  6. Pedestrian volumes vary Our meso-level, dynamic assignment model does not simulate pedestrians. It does not know that right turns at “A” are more restricted than at “B” Intersection A Intersection B SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  7. Methodologies • Analytical methods • Simulation methods • Local observations SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  8. Analytical approaches SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  9. Analytical approaches SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  10. Pedestrian volume – vehicle flow relationship • Assumes 2 sec veh-veh headway, 50% green time • Doesn’t allow for ped volumes > 5,000 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  11. Comparing different methods HCM2010 Rouphail and Eads, 1997 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  12. Applying pedestrian friction in the model • Green time • Saturation flow rate Follow-up time SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  13. Scale of Application Area Type Method Neighborhood Type Method Uniform Parameters Method Unique Intersection Method 55% Existing approach 74% Under consideration Do nothing approach 87% Under consideration SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  14. Resources • SFMTA pedestrian count program • SFMTA pedestrian model • Project-related pedestrian counts • Pedestrian-vehicle observations SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  15. SFMTA pedestrian count program • 2-hour pedestrian counts at 50 locations (2009/2010) • Six automatic pedestrian counters (24/7 observations) Actual pedestrian count binder SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  16. Automated pedestrian counters • Time and day profiles Tenderloin Union Square Castro SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  17. Pedestrian count locations SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  18. HCM 2010 flow rate adjustment factors SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  19. SFMTA pedestrian model • Used count data and log-linear regression model to estimate pedestrian volumes throughout San Francisco lnYi= β0+ β1X1i+ β2X2i+ … + βjXji • Combined with auto traffic and collision data to develop pedestrian crossing accident risk factors SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  20. Pedestrian volume estimates SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  21. Local validation of pedestrian estimation SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  22. Pedestrian-vehicle interaction observations SFCTA staff observed downtown intersections of varying pedestrian volumes Collected information on quantity of pedestrians and relative restriction of vehicle turning movement capacity The urban environment is complex: • Groupings of peds, directions of travel bicycles, unique timing plans, variations in geometry, etc. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  23. Observed pedestrian-vehicle interactions Incremental pedestrians have the greatest impact at low pedestrian volumes SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  24. Observed pedestrian-vehicle interactions HCM2010 Rouphail and Eads, 1997 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  25. Model Tests • Uniform parameters method • Area type method • Neighborhood type method – sat flow rate • Neighborhood type method – green time reduction SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  26. Focus on pedestrian count-rich area • SoMa pedestrian count locations • 4th / Market • 6th / Market • 8th / Market • 6th / Mission • 3rd / Howard • 7th / Folsom 1 5 2 4 3 6 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  27. Hourly pedestrian counts 7th St 5th St 3rd St Market St 10,000 3,000 3,300 Mission St 1,100 Howard St 1,200 Folsom St 600 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  28. Network parametersUniform parameter method Sat flow 1,800 Spacing 2.0s All Signals SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  29. Network parametersArea type method Sat flow 1,620 Spacing 2.22s Hi Peds Med Peds Sat flow 1,710 Spacing 2.11s SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  30. Network parametersNeighborhood type method Adjust saturation flow rates Spacing 4.4s Sat Flow 180 Spacing 20s Sat Flow 810 Sat Flow 1,260 Spacing 2.9s Sat Flow 1,530 Spacing 2.4s SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  31. Network parametersNeighborhood type method Reduce green time for turning movements 90% less green 55% less green 30% less green 15% less green SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  32. Testing pedestrian friction methods Area type method • Evaluation not yet complete! • Numbers At select subarea nodes, turning movement volume validation improves with neighborhood type method • Queuing Neighborhood method seems to produce more realistic queue formation than area method Neighborhood type method SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  33. Conclusions • Pedestrian-vehicle friction is very important at some locations! • Numerous options to make assignment models sensitive to pedestrians • The most realistic approaches are very difficult • We still have work to do: • Run more tests • More local validation • Develop better neighborhood system • Incorporate time of day factors • Develop strategy for scenarios that change pedestrian volumes SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

  34. The End. Questions? SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

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