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Incorporation of Capacity Constraints, Crowding, and Reliability in Transit Forecasting

Incorporation of Capacity Constraints, Crowding, and Reliability in Transit Forecasting. Peter Vovsha, Bill Davidson, Gaurav Vyas, PB Marcelo Oliveira, Michael Mitchell, GeoStats Chaushie Chu, Robert Farley, LACMTA. Capacity Constraint & Crowding Effects Intertwined.

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Incorporation of Capacity Constraints, Crowding, and Reliability in Transit Forecasting

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  1. Incorporation of Capacity Constraints, Crowding, and Reliability in Transit Forecasting Peter Vovsha, Bill Davidson, Gaurav Vyas, PB Marcelo Oliveira, Michael Mitchell, GeoStats Chaushie Chu, Robert Farley, LACMTA TPAC, Columbus, OH, May 5-9, 2013

  2. Capacity Constraint & Crowding Effects Intertwined • Capacity constraint (demand exceeds total capacity) • Riders cannot board the vehicle and have to wait for the next one • Modeled as effective line-stop-specific headway greater than the actual one • Similar to shadow pricing in location choices or VDF when V/C>1 • Crowding inconvenience and discomfort (demand exceeds seated capacity): • Some riders have to stand • Seating passengers experience inconvenience in finding a seat and getting off the vehicle • Modeled as perceived weight factor on segment IVT TPAC, Columbus, OH, May 5-9, 2013

  3. Effective Headway Calculation (Line & Stop Specific) Board Δ Capacity= Total capacity- Volume+Alight Volume Stop Stop Eff.Hdwy Factor Alight 1 0 1 Board/ΔCap TPAC, Columbus, OH, May 5-9, 2013

  4. Critical Points of Crowding Function TPAC, Columbus, OH, May 5-9, 2013

  5. Transit Reliability Measures • Schedule adherence at boarding stop (extra wait time) • Impact of congestion (extra IVT) • Combined lateness at destination versus planned arrival time (similar to auto) 1 2 3 TPAC, Columbus, OH, May 5-9, 2013

  6. SP Design & Implementation • Survey Platform: GeoStats’ Web GeoSurvey • Supports complex skip logic, computed questions, recalls and rosters • Unlimited questionnaire size • Fully translatable • Can be customized and integrated with other technologies to fit project needs • Survey Design • Combined RP survey and SP games into a single self-complete WEB instrument • First collected single one way trip information and then generated scenarios based on it • Integrated geocoding of OD using Google Maps • Obtained itinerary alternatives directly from Metro’s trip planner • Complex logic for game generation also made use of pre-computed LOS skims • Survey Fielding • Metro placed placards in vehicles inviting riders to participate • Social media and email distribution lists used to drive participants to survey • Participant feedback motivated design revisions and simplification of SP games • Cash incentive ($250) paid once a week using a random draw TPAC, Columbus, OH, May 5-9, 2013

  7. Web GeoSurvey TPAC, Columbus, OH, May 5-9, 2013

  8. Web GeoSurvey TPAC, Columbus, OH, May 5-9, 2013

  9. Web GeoSurvey TPAC, Columbus, OH, May 5-9, 2013

  10. Crowding Levels TPAC, Columbus, OH, May 5-9, 2013

  11. SP Stats • 2,500 usable responses • 6-9 games per respondent • 2 observed choices per game: • 1st ranked Alt over 2nd and 3rd • 2nd ranked Alt over 3rd • 30,000 usable observations TPAC, Columbus, OH, May 5-9, 2013

  12. Person Distribution TPAC, Columbus, OH, May 5-9, 2013

  13. Observed Trip Distribution TPAC, Columbus, OH, May 5-9, 2013

  14. Reported Crowding & Reliability TPAC, Columbus, OH, May 5-9, 2013

  15. Crowding Effects Summary • Hypotheses confirmed: • Crowding perceived as extra IVT weight • Crowding is more onerous for commuters • Crowding more onerous for older riders • Crowding perceived differentially by mode • Hypotheses not confirmed: • Crowding more onerous for high incomes • Crowding weight grows with trip length TPAC, Columbus, OH, May 5-9, 2013

  16. Trip Length Effect • It might look counter-intuitive that crowding IVT weight does not grow with trip length • However, even if the weight is constant the resulted crowding penalty does grow with trip length: • IVT weight 1.5 • 10 min in crowded vehicle equivalent to 5 extra min • 60 min in crowded vehicle equivalent to 30 extra min • Logit models are sensitive to differences, thus trip length manifests itself in crowding-averse behavior TPAC, Columbus, OH, May 5-9, 2013

  17. General Functional Form for Crowding IVT Weight Weight=1+(1-SeatProb)3.4×1.58 TPAC, Columbus, OH, May 5-9, 2013

  18. Segmentation of Crowding IVT Weight – Trip Purpose TPAC, Columbus, OH, May 5-9, 2013

  19. Segmentation of Crowding IVT Weight – Person Age TPAC, Columbus, OH, May 5-9, 2013

  20. Segmentation of Crowding IVT Weight – Household Income TPAC, Columbus, OH, May 5-9, 2013

  21. Segmentation of Crowding IVT Weight – Transit Mode TPAC, Columbus, OH, May 5-9, 2013

  22. Reliability Impact: Expected Delay (Linear Formulation) • Calculated as Amount×Frequency • Weight vs. non-crowded IVT is 1.76 • Confirms negative perception beyond just extension of IVT TPAC, Columbus, OH, May 5-9, 2013

  23. Illustration of Linear Formulation TPAC, Columbus, OH, May 5-9, 2013

  24. Possible Non-Linear Effects • Amount of delay: • Discarding small delays, avoiding big delays (convexity) • Adaptation to big delays (concavity) • Frequency of delay: • Discarding infrequent delays, avoiding frequent delays (convexity) • Adaptation to frequent delays (concavity) TPAC, Columbus, OH, May 5-9, 2013

  25. Best Statistical Form -0.142×Delay×Freq (base linear) +0.091×Delay×Freq2 (freq convex) +0.161×Delay0.5×Freq (delay concave) TPAC, Columbus, OH, May 5-9, 2013

  26. Amount of Delay Effect Convexity, discarding very small delays TPAC, Columbus, OH, May 5-9, 2013

  27. Frequency of Delay Effect Concavity, adaptation TPAC, Columbus, OH, May 5-9, 2013

  28. 6 Travel Time Components TPAC, Columbus, OH, May 5-9, 2013

  29. Passenger Split between Attractive Lines Standard combined frequency approach Logit discrete choice Line share ~ Effective Frequency × Discount Schedule wait Capacity wait Unreliability wait Physical IVT Crowding IVT Unreliability IVT TPAC, Columbus, OH, May 5-9, 2013

  30. Conclusions • Capacity constraints, crowding, and reliability can be effectively incorporated in travel model: • Transit assignment • Model choice • Essential for evaluation of transit projects: • Capacity relief • Real attractiveness for the user • Explanation of weird observed choices (driving backward to catch a seat) TPAC, Columbus, OH, May 5-9, 2013

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