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

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.