Public Transport Network Design for Equality of Accessibility via Message Passing Neural Networks and Reinforcement Learning

Duo Wang, Andrea Araldo, Maximilien Chau

2025

Abstract

Graph learning involves embedding relevant information about a graph’s structure into a vector space. However, graphs often represent objects within a physical or social context, such as a Public Transport (PT) graph, where nodes represent locations surrounded by opportunities. In these cases, the performance of the graph depends not only on its structure but also on the physical and social characteristics of the environment. Optimizing a graph may require adapting its structure to these contexts. This paper demonstrates that Message Passing Neural Networks (MPNNs) can effectively embed both graph structure and environmental information, enabling the design of PT graphs that meet complex objectives. Specifically, we focus on accessibility, an indicator of how many opportunities can be reached in a unit of time. We set the objective to design a “equitable” PT graph with a lower accessibility inequality. We combine MPNN with Reinforcement Learning (RL) and show the efficacy of our method against metaheuristics in a use case representing in simplified terms the city of Montreal. Our superior results show the capacity of MPNN and RL to capture the intricate relations between the PT graph and the environment, which metaheuristics do not achieve.

Paper Citation

in Harvard Style

Wang D., Araldo A. and Chau M. (2025). Public Transport Network Design for Equality of Accessibility via Message Passing Neural Networks and Reinforcement Learning. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 619-630. DOI: 10.5220/0013166000003890

in Bibtex Style

@conference{icaart25,
author={Duo Wang and Andrea Araldo and Maximilien Chau},
title={Public Transport Network Design for Equality of Accessibility via Message Passing Neural Networks and Reinforcement Learning},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={619-630},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013166000003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Public Transport Network Design for Equality of Accessibility via Message Passing Neural Networks and Reinforcement Learning
SN - 978-989-758-737-5
AU - Wang D.
AU - Araldo A.
AU - Chau M.
PY - 2025
SP - 619
EP - 630
DO - 10.5220/0013166000003890
PB - SciTePress