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

TL;DR

This paper presents a novel approach combining Message Passing Neural Networks and Reinforcement Learning to design public transport networks that reduce inequality in accessibility across urban areas.

Contribution

It introduces a new method focusing on accessibility equality in public transport network design, outperforming traditional metaheuristics.

Findings

Effective reduction of accessibility inequality in case study

Outperforms classical metaheuristics in network design

Demonstrates potential for urban sustainability improvements

Abstract

Designing Public Transport (PT) networks able to satisfy mobility needs of people is essential to reduce the number of individual vehicles on the road, and thus pollution and congestion. Urban sustainability is thus tightly coupled to an efficient PT. Current approaches on Transport Network Design (TND) generally aim to optimize generalized cost, i.e., a unique number including operator and users' costs. Since we intend quality of PT as the capability of satisfying mobility needs, we focus instead on PT accessibility, i.e., the ease of reaching surrounding points of interest via PT. PT accessibility is generally unequally distributed in urban regions: suburbs generally suffer from poor PT accessibility, which condemns residents therein to be dependent on their private cars. We thus tackle the problem of designing bus lines so as to minimize the inequality in the geographical distribution of accessibility. We combine state-of-the-art Message Passing Neural Networks (MPNN) and Reinforcement Learning. We show the efficacy of our method against metaheuristics (classically used in TND) in a use case representing in simplified terms the city of Montreal.