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Geo-spatial Analysis in Transit Demand Estimation utilizing ITS Applications

Geo-spatial Analysis in Transit Demand Estimation utilizing ITS Applications Peter Bang, Ph.D., AEI. GIS in Transit Conference October 16, 2013 Washington, DC. Contents. Introduction of This Study RTC’s ITS Applications GIS Analysis What We Found. Introduction of This Study.

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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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