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Jesper Bláfoss Ingvardson 1 , Otto Anker Nielsen 1 , Sebastián Raveau 2 , Bo Friis Nielsen 1

The influence of transit service frequency and station characteristics on passenger arrival time distributions: A smart card data analysis in the Greater Copenhagen Area. Trafikdage , Aalborg – 28 August 2017. Jesper Bláfoss Ingvardson 1 , Otto Anker Nielsen 1 ,

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Jesper Bláfoss Ingvardson 1 , Otto Anker Nielsen 1 , Sebastián Raveau 2 , Bo Friis Nielsen 1

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  1. The influence of transit service frequency and station characteristics on passenger arrival time distributions:A smart card data analysis in the Greater Copenhagen Area Trafikdage, Aalborg – 28 August 2017 Jesper Bláfoss Ingvardson1, Otto Anker Nielsen1, Sebastián Raveau2, Bo Friis Nielsen1 1 Technical University of Denmark 2PontificiaUniversidadCatólica de Chile

  2. Agenda • Motivation & Research objective • Methodologicalframework • Case study & Data • Data cleaning & Validation • Results • Conclusions

  3. Motivation and research objective • Motivation • Passenger waiting times important due to highervalue of time • Increasedavailability of real-time information and on-demand public travel planners encouragepassengers to time their arrival at stations • Traditionalpublishedtimetables vs. frequency-basedtimetable • Research objective • Developmethodology for analysingpassenger arrivals thatexplicitlytakeintoaccountpassengersarrivingrandomly and non-randomly • Analyse the influence of station characteristics and amenities on passenger arrival patterns

  4. Methodologicalframework (I) • Two types of passengers:

  5. Methodologicalframework (II) • Random arrivals • Thosearrivingrandomly, e.g. without knowing the timetable • Adoptedtraditionalapproach to model arrivals as uniformlyrandom

  6. Methodologicalframework (III) • Non-random arrivals • Those timing their arrival according to the timetable • Adda buffer to not miss the departure • Modelled as Beta-distribution • Bounded on interval • Can handle passengers’ access time buffers • General form of the uniform distribution

  7. Case study & data • Smart card data (Rejsekort) • 100 million public transport trips annually (Rejsekort A/S, 2017) • ~1 million used (Sep-Oct 2014, onlyfirst trip leg on weekdays) • Tap-in-tap-out on station platforms (buses excluded) • Tap-in at arrival (?) • Sample bias (fewcommuters and students) • Validatedagainst manual observations of passenger arrivals • Timetable data for trains (Suburban and regional) • Synthetictimetable for metro (nopublishedtimetable)

  8. Data cleaningconsiderations … • Important to takeintoaccountrealisedtimetable

  9. Data cleaningconsiderations … • Important to takeintoaccountrealisedtimetable

  10. Data cleaningconsiderations … • Important to takeintoaccount S-traindwell times

  11. Validation • Comparison of Rejsekort data and manually collected arrival data at Bernstorffsvej station on August 11, 2016

  12. Results • Frequency-based vs publishedtimetable

  13. Results • Frequency-based vs publishedtimetable

  14. Results – Mixture distributions • Headway time: 5 minutes

  15. Results – Mixture distributions • Headway time: 10 minutes

  16. Results – Mixture distributions • Headway time: 20 minutes

  17. Results – Mixture distributions • Headway time: 30 minutes

  18. Results – Mixture distributions • Headway time: 60 minutes

  19. Results – Overview • The lower the frequency the more timed arrivals

  20. Results – Station characteristics

  21. Results – Station characteristics • Station layout important for passenger waiting times • Difference between Time-Of-Day • Probablyrelated to travel purpose

  22. Results – Station characteristics • Station layout important for passenger waiting times • Difference between Time-Of-Day • Probablyrelated to travel purpose

  23. Results – Station characteristics • Station layout important for passenger waiting times • Difference between Time-Of-Day • Probablyrelated to travel purpose Percentageuniformly random

  24. Results – Station characteristics • Station layout important for passenger waiting times • Difference between Time-Of-Day • Probablyrelated to travel purpose Avg. waiting times

  25. Conclusions • Conclusions • Waiting time distribution canbe modelled as a mixture of Uniform and Beta distributions • The lower the frequency the more timed arrivals • Implications • Important to provide real timetables to passengers • Framework canimprove waiting time estimations in transport models

  26. Thank you for your attention! Jesper Bláfoss Ingvardson Ph.d.-student, Technical University of Denmark jbin@dtu.dk

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