TPAC, Columbus, OH, May 5-9, 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 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

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

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

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,

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 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

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

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

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

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,

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,

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

Observed Trip Distribution TPAC, Columbus, OH, May 5-9, 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 min Between 10 to 19 mins Between 20 to 29 mins Between 30 to39 mins Between 40 to49 mins More than 49 mins Total

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

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,

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 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,

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

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

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

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

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

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,

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

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,

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

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

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

6 Travel Time Components TPAC, Columbus, OH, May 5-9, ComponentWaitIVTWeightCalculated for each line Combined for strategy & skimming Scheduled waitX calibrated 0.5 HeadwayCombined headway Extra wait due capacity restraint X calibrated 0.5 Effective headway Combined headway Unreliability extra wait X SPRegressionWeighted average Physical scheduled IVT X Calibrated Transit time function Weighted average Perceived crowding inconvenience XEntire component SP Crowding function SP Weighted average Unreliability IVT delay X SPRegressionWeighted average

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

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,