One Rule to Bring Them All: Investigating Transport Connectivity in Public Transport Route Generation for Equitable Access

TL;DR

This paper explores AI-driven methods to design equitable and resilient public transport networks, introducing a new connectivity-aware metric to improve accessibility and social fairness in urban transit planning.

Contribution

It introduces a transport connectivity-aware accessibility metric and evaluates hybrid neuroevolutionary methods for scalable, equitable transit network design.

Findings

Improved network resilience on synthetic datasets

Enhanced algebraic connectivity through optimization

Challenges in applying methods to real urban data

Abstract

Designing a city-wide public transport network poses a dual challenge: achieving computational efficiency while ensuring spatial equity for different population groups. We investigate whether AI-based optimization hybrid neuroevolutionary methods combining graph neural networks with evolutionary algorithms - can scale Transit Network Design Problem (TNDP) solutions from synthetic tests to real urban networks while preserving social fairness. Our contribution is to introduce a transport connectivity-aware accessibility metric that bases optimization on principles of equitable accessibility rather than traditional trade-offs between passenger and operator costs. The results show a noticeable improvement in network resilience by improving algebraic connectivity on synthetic datasets, and highlight the ambiguity of applying network generation to real data.