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Authors: Maria Elsa 1 ; Hung-Jui Chang 2 ; Da-Wei Wang 3 ; Chih-Wen Hsueh 1 and Tsan-sheng Hsu 3

Affiliations: 1 Department of Computer Science and Engineering, National Taiwan University, Taiwan ; 2 Deparement of Computer Science and Engineering, National Dong Hwa University, Taiwan ; 3 Institute of Information Science, Academia Sinica, Taiwan

Keyword(s): Public Transit, Agent-Based Simulation, Multi-Agent Reinforcement Learning, Shortest Path, Multi-Agent Path Finding.

Abstract: We investigate the problem of providing coordinated route recommendations to subway passengers to reduce peak-hour congestion and improve social distancing during a pandemic such as COVID-19. We develop TransMARL, a model-free method that combines multi-agent reinforcement learning and curriculum learning to learn optimal routing policies by interacting with the environment. Furthermore, TransMARL is simple in design and adapts the framework of centralized training with decentralized execution. Applying TransMARL to the busy Taipei Metro network with more than 2 million daily ridership, our simulation result shows that overcrowded passengers can be reduced by more than 50% with less than 10 minutes increasing traveling time when 20% or more passengers follow the provided route guidance. The result outperforms previous well-known transit assignment methods, e.g., the all-or-nothing and stochastic user equilibrium.

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Paper citation in several formats:
Elsa, M.; Chang, H.; Wang, D.; Hsueh, C. and Hsu, T. (2024). Coordinated Route Recommendation for Improving Social Distancing in a Congested Subway Network. In Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-708-5; ISSN 2184-2841, SciTePress, pages 27-36. DOI: 10.5220/0012709800003758

@conference{simultech24,
author={Maria Elsa. and Hung{-}Jui Chang. and Da{-}Wei Wang. and Chih{-}Wen Hsueh. and Tsan{-}sheng Hsu.},
title={Coordinated Route Recommendation for Improving Social Distancing in a Congested Subway Network},
booktitle={Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2024},
pages={27-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012709800003758},
isbn={978-989-758-708-5},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - Coordinated Route Recommendation for Improving Social Distancing in a Congested Subway Network
SN - 978-989-758-708-5
IS - 2184-2841
AU - Elsa, M.
AU - Chang, H.
AU - Wang, D.
AU - Hsueh, C.
AU - Hsu, T.
PY - 2024
SP - 27
EP - 36
DO - 10.5220/0012709800003758
PB - SciTePress

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