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Modelling and simulation of rail passengers to evaluate methods to reduce dwell times

Perkins, Adam; Ryan, Brendan; Siebers, Peer-Olaf

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Authors

Adam Perkins



Abstract

The paper outlines a feasibility study using modelling and simulation to reduce dwell times and increase rail network capacity. We use agent based modelling, where passengers are treated as a separate entities, basing their movements on rules from the Social Force Model (SFM), proposed by Helbing to model pedestrian dynamics. Implementing this SFM, together with a novel decision making system for passengers' door choices, a mesoscopic model is produced of the platform, train and passengers. An outline of the modelling process is presented, along with a critical analysis of the final model. Analyses are conducted to evaluate novel concepts in train and platform design, to reduce loading times, using passengers with a range of attributes. In a simulation experiment, four concepts (wider doors, designated boarding/alighting doors, and an active passenger information system) are assessed, with the latter two giving reductions in loading times of 7.0% and 7.3%.

Citation

Perkins, A., Ryan, B., & Siebers, P.-O. Modelling and simulation of rail passengers to evaluate methods to reduce dwell times. Presented at 14th International Conference on Modeling and Applied Simulation, MAS 2015

Conference Name 14th International Conference on Modeling and Applied Simulation, MAS 2015
End Date Sep 23, 2015
Acceptance Date May 21, 2015
Publication Date Sep 23, 2015
Deposit Date Sep 12, 2016
Publicly Available Date Sep 12, 2016
Peer Reviewed Peer Reviewed
Keywords agent-based modelling, simulation, rail passengers, dwell time reduction
Public URL https://nottingham-repository.worktribe.com/output/760758
Related Public URLs http://toc.proceedings.com/28112webtoc.pdf
Additional Information Published in: Proceedings of the International Conference on Modeling and Applied Simulation 2015 / Agostino G. Bruzzone ... [et al.], eds. Rende, Italy : CAL-TEK, c2015, p. 132-141. ISBN 9781510813861.
Contract Date Sep 12, 2016

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