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Geo-spatial Analysis in Transit Demand Estimation utilizing ITS Applications

Explore the use of ITS applications in geo-spatial analysis for transit demand estimation, including data collection from GPS/APC, route adjustments, and regression modeling. Case study focuses on Reno/Sparks area transit system.

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Geo-spatial Analysis in Transit Demand Estimation utilizing ITS Applications

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  1. Geo-spatial Analysis in Transit Demand Estimation utilizing ITS Applications Peter Bang, Ph.D., AEI GIS in Transit Conference October 16, 2013 Washington, DC

  2. Contents • Introduction of This Study • RTC’s ITS Applications • GIS Analysis • What We Found

  3. Introduction of This Study Current Transit Demand Estimation • Conventional Travel Demand Model • Simple Linear Est. based on Land Use, Route LOS, Historic Data • Sketchy Route Assignments by Experts, Surveys • Try and Error Adjustment based on Experts’ Expertise • Multiple non-linear Regression Est. with AVL, APC, & Parcel-Level Land Use Data

  4. Reno/Sparks : The Study Area

  5. RTC’s Bus Routes in 2012

  6. RTC’sTransit Operation

  7. RTC’s Current ITS Application • Automatic Vehicle Location (AVL) on approximately 147 fixed-route, paratransit and supervisor vehicles • Automatic Passenger Counters(APC) • Transit Signal Priority (TSP) installed on at least 56 fixed-route vehicles • Computer-Aided Dispatch • Real-Time Traveler Information • Web based Trip Planning tool

  8. Contents • Raw Data (with all the dots)

  9. Example of Raw Data from ITS

  10. Bus Stops

  11. Raw Data From GPS/APC

  12. Raw Data From GPS/APC

  13. Bus Stops

  14. Demand Points

  15. Demand Points

  16. Demand Points in Thiessen/Buffer Area

  17. Demand Points in Buffer Area

  18. Demand Points in Thiessen-Buffer Area

  19. Demand Points in Thiessen-Buffer Area

  20. Dwelling Units

  21. Dwelling Units Around Demand Points

  22. Employments by 6 Categories

  23. Dwelling Units & Employments Around Demand Points

  24. Demand Estimation I • All 575 Demand Points are IN; • = • (.000)* (.000)* • = 0.965 • = in .05 level significance

  25. Demand Estimation I

  26. Demand Estimation I y = 88.631 + 4.8737·(# of Routes)3R² = 0.9652

  27. Demand Estimation I 4th St. Station, 18 routes y = 88.631 + 4.8737·(# of Routes)3R² = 0.9652

  28. Demand Estimation I 4th St. Station, 18 routes Meadowood Mall, 8 routes y = 88.631 + 4.8737·(# of Routes)3R² = 0.9652

  29. Demand Estimation I 4th St. Station, 18 routes Meadowood Mall, 8 routes Centennial Plaza, 6 routes y = 88.631 + 4.8737·(# of Routes)3R² = 0.9652

  30. Demand Estimation II = -51.615Ret8th (.001)* (.000)* (.000)* (.000)* (.000)* (.000)* (.008)* (.022)* = 0.592 = in .05 level significance Conditions ; SELECT IF (rank >= 4). SELECT IF (Num_Routes>= 2 ). SELECT IF (DU_8 > median ). SELECT IF (SUM_TOT > 0 ).

  31. Demand Estimation II = -0.4 (.098)* (.000)* (.000)* Acre84 (.000)* (.004)* (.026)* = 0.632 = in .05 level significance Conditions ; SELECT IF (rank >= 4). SELECT IF (Num_Routes>= 2 ). SELECT IF (Emp_8 > median ). SELECT IF (SUM_TOT > 0 ).

  32. Demand Estimation II = -11.5 (.066)* (.000)* (.000)* Acre84 (.003)* (.019)* = 0.623 = in .05 level significance Conditions ; SELECT IF (rank >= 4). SELECT IF (Num_Routes>= 2 ). SELECT IF (Emp_4 > median ). SELECT IF (SUM_TOT > 0 ).

  33. Demand Estimation II

  34. Model Output Validation

  35. Model Output Validation

  36. Model Application to Future Routes

  37. Model Output Validation

  38. Contents Future Routes Existing Routes

  39. Further Study • Needs More Understanding on Data • Income, Captive Riders, Alternative Modes • New Mobility Indexes of Each Routes • Refined Accessibility Indexes of Each Stops • Transit LOSs ; Total Service Area, Fare System, Headways, etc.

  40. Acknowledgement • Jeremy Smith, Lee Gibson, Amy Cummings • Tom Kowalski(UTA)

  41. Q/A Thank You For Your Time !! Peter Bang, Ph.D. 202-366-2317

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