A passenger-pedestrian model to assess platform and train usage from automated data

•We describe crowding on-board and at stations based on automated transit data.•Travelers consider the influence of crowding on comfort and on walking speeds.•AA Dutch rail corridor with a rich set of passenger data is studied.•Pedestrian flows, densities and train car loads are well reproduced.•App...

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Bibliographic Details
Published in:Transportation research. Part A, Policy and practice Policy and practice, 2020-02, Vol.132, p.948-968
Main Authors: Hänseler, Flurin S., van den Heuvel, Jeroen P.A., Cats, Oded, Daamen, Winnie, Hoogendoorn, Serge P.
Format: Article
Language:English
Subjects:
Crowding
Investment appraisal
Level-of-service assessment
Public transportation
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Summary:•We describe crowding on-board and at stations based on automated transit data.•Travelers consider the influence of crowding on comfort and on walking speeds.•AA Dutch rail corridor with a rich set of passenger data is studied.•Pedestrian flows, densities and train car loads are well reproduced.•Applications include transit planning, crowding estimation, or disruption management. We present a transit model that, based on automated fare collection and train tracking data, describes pedestrian movements in train stations and vehicle-specific train ridership distributions. Our approach differs from existing models in that we describe on-board passenger dynamics and pedestrian dynamics at stations in a joint framework. We assume that travelers first decide on the train(s) they will take, and then pursue their journey through the network by successively choosing pedestrian paths, waiting positions on platforms, and specific train cars. Travelers explicitly maximize their travel utility. We model how crowding influences their walking speeds, and how it affects travel utility both at stations and on-board. To illustrate the framework, we present a real case study of a major Dutch rail corridor, for which we collect a rich set of passenger, pedestrian and train operation data. We observe a good agreement of model estimates with empirical observations, and discuss the use of the model for various transit-related problems including level-of-service assessment, crowding estimation, transit optimization, and integrated investment appraisal.
ISSN:0965-8564
1879-2375
DOI:10.1016/j.tra.2019.12.032