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

Flurin S Hänseler,Winnie Daamen,Serge P Hoogendoorn,Jeroen P.A Van Den Heuvel,Oded Cats

doi:10.1016/j.tra.2019.12.032

Flurin S Hänseler, Winnie Daamen + Show 3 more

Open Access

https://doi.org/10.1016/j.tra.2019.12.032

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Abstract

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.

Highlights

A key characteristic of transit systems is their level of crowding, i.e., the accumulation of travelers on platforms, access ways, and in trains (Haywood et al, 2017)
Our approach differs from existing models in that we describe on-board passenger dynamics and pedestrian dynamics at stations in a joint framework
Our approach is inspired by agent-based transit assignment models, which we extend by a pedestrian model to capture walking and waiting behavior in stations

Summary

Introduction

A key characteristic of transit systems is their level of crowding, i.e., the accumulation of travelers on platforms, access ways, and in trains (Haywood et al, 2017). The number of travelers in a transit system is subject to strong spatiotemporal fluctuations (Hermant, 2012). Understanding these fluctuations, as well as the underlying interactions between travelers, infrastructure and rolling stock, is of key relevance to improve safety, comfort and efficiency (Raveau et al, 2014). We briefly present related transit assignment models, focusing on simulation-based and data-driven approaches, as well as pedestrian movement models for train stations. We propose a new approach that is hybrid in that it considers both the network and station level, and in that it contains both model- and data-driven components. We distinguish agent-based models and data-driven approaches

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