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TPAC, Columbus, OH, May 5-9, 20131 Incorporation of Capacity Constraints, Crowding, and Reliability in Transit Forecasting Peter Vovsha, Bill Davidson,

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Presentation on theme: "TPAC, Columbus, OH, May 5-9, 20131 Incorporation of Capacity Constraints, Crowding, and Reliability in Transit Forecasting Peter Vovsha, Bill Davidson,"— Presentation transcript:

1 TPAC, Columbus, OH, May 5-9, 20131 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

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, 20132

3 Effective Headway Calculation (Line & Stop Specific) TPAC, Columbus, OH, May 5-9, 20133 Stop Volume Alight Board Δ Capacity= Total capacity- Volume+Alight Board/ΔCap Eff.Hdwy Factor 0 1 1

4 Critical Points of Crowding Function TPAC, Columbus, OH, May 5-9, 20134

5 Transit Reliability Measures 1.Schedule adherence at boarding stop (extra wait time) 2.Impact of congestion (extra IVT) 3.Combined lateness at destination versus planned arrival time (similar to auto) TPAC, Columbus, OH, May 5-9, 20135 1 2 3

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, 20136

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

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

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

10 Crowding Levels Crowding levelProbability of having a seat Verbal description 1100% (5 out of 5 trips)Not crowded 280% (4 out of 5 trips)Slightly crowded 360% (3 out of 5 trips)Somewhat crowded 440% (2 out of 5 trips)Crowded 520% (1 out of 5 trips)Very crowded 60% (0 out of 5 trips)Extremely crowded 70% (0 out of 5 trips) 1 out of 5 trips unable to board Extremely crowded TPAC, Columbus, OH, May 5-9, 201310

11 SP Stats 2,500 usable responses 6-9 games per respondent 2 observed choices per game: 1 st ranked Alt over 2 nd and 3 rd 2 nd ranked Alt over 3 rd 30,000 usable observations TPAC, Columbus, OH, May 5-9, 201311

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

13 Observed Trip Distribution TPAC, Columbus, OH, May 5-9, 201313 In-Vehicle Time Destination Purpose Home activities Work activities School activities Shopping Personal business Visiting doctor or dentist Leisure, entertainment, or dining out Visiting others OtherTotal Less than 10 min321043114 4271015251 Between 10 to 19 mins 5823775324115302137546 Between 20 to 29 mins 4926156173312451935527 Between 30 to39 mins56223561425526620431 Between 40 to49 mins3617233817922624327 More than 49 mins 4223956223515582436527 Total 273123630710716560208861672609

14 Reported Crowding & Reliability TPAC, Columbus, OH, May 5-9, 201314

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, 201315

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, 201316

17 General Functional Form for Crowding IVT Weight TPAC, Columbus, OH, May 5-9, 201317 Weight=1+(1-SeatProb) 3.4 ×1.58

18 Segmentation of Crowding IVT Weight – Trip Purpose TPAC, Columbus, OH, May 5-9, 201318

19 Segmentation of Crowding IVT Weight – Person Age TPAC, Columbus, OH, May 5-9, 201319

20 Segmentation of Crowding IVT Weight – Household Income TPAC, Columbus, OH, May 5-9, 201320

21 Segmentation of Crowding IVT Weight – Transit Mode TPAC, Columbus, OH, May 5-9, 201321

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, 201322

23 Illustration of Linear Formulation TPAC, Columbus, OH, May 5-9, 201323

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, 201324

25 Best Statistical Form -0.142×Delay×Freq (base linear) +0.091×Delay×Freq 2 (freq convex) +0.161×Delay 0.5 ×Freq (delay concave) TPAC, Columbus, OH, May 5-9, 201325

26 Amount of Delay Effect TPAC, Columbus, OH, May 5-9, 201326 Convexity, discarding very small delays

27 Frequency of Delay Effect TPAC, Columbus, OH, May 5-9, 201327 Concavity, adaptation

28 6 Travel Time Components TPAC, Columbus, OH, May 5-9, 201328 ComponentWaitIVTWeightCalculated for each line Combined for strategy & skimming Scheduled waitX2.0-2.5 calibrated 0.5 HeadwayCombined headway Extra wait due capacity restraint X2.0-3.0 calibrated 0.5 Effective headway Combined headway Unreliability extra wait X2.0-3.0 SPRegressionWeighted average Physical scheduled IVT X0.85-1.00 Calibrated Transit time function Weighted average Perceived crowding inconvenience XEntire component SP Crowding function SP Weighted average Unreliability IVT delay X2.0-3.0 SPRegressionWeighted average

29 Passenger Split between Attractive Lines TPAC, Columbus, OH, May 5-9, 201329 Line shareEffective FrequencyDiscount ×~ Schedule wait Capacity wait Unreliability wait Physical IVT Crowding IVT Unreliability IVT Standard combined frequency approach Logit discrete choice

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, 201330


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