An agent-based model for modal shift in public transport
Abstract
Modal shift in public transport as a consequence of a disruption on a line has in some cases unforeseen consequences such as an increase in congestion in the rest of the network. How information is provided to users and their behavior plays a central role in such configurations. We introduce here a simple and stylised agent-based model aimed at understanding the impact of behavioural parameters on modal shift. The model is applied on a case study based on a stated preference survey for a segment of Paris suburban train network. We systematically explore the parameter space and show non-trivial patterns of congestion for some values of discrete choice parameters linked to perceived wait time and congestion. We also apply a genetic optimisation algorithm to the model to search for optimal compromises between congestion in different modes.
Cite
@article{arxiv.2107.11399,
title = {An agent-based model for modal shift in public transport},
author = {Thibaut Barbet and Amine Nacer-Weill and Changtao Yang and Juste Raimbault},
journal= {arXiv preprint arXiv:2107.11399},
year = {2021}
}
Comments
8 pages, 4 figures