2024/04/08 by Andrew Holliday, Ahmed El-Geneidy, Holliday, Andrew +3 · 1 citation
Engineering · Social Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Traffic Prediction and Management Techniques #Transportation Planning and Optimization
paper · pdf · doi:10.48550/arxiv.2404.05894
openalex publication_date 2024/04/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Planning a network of public transit routes is a challenging optimization problem. Metaheuristic algorithms search through the space of possible transit networks by applying heuristics that randomly alter routes in a network. Existing algorithms almost exclusively use heuristics that modify the network in purely random ways. In this work, we explore whether we can obtain better transit networks using more intelligent heuristics, that modify networks according to a learned preference function instead of at random. We use reinforcement learning to train graph neural nets to act as heuristics. These neural heuristics yield improved results on benchmark synthetic cities with 70 nodes or more, and achieve new state-of-the-art results on the challenging Mumford benchmark. They also improve upon a simulation of the real transit network in the city of Laval, Canada, achieving cost savings of up to 19% over the city's existing transit network.