Learning to Design City-scale Transit Routes

TL;DR

This paper introduces a reinforcement learning framework using graph attention networks to automatically design city-scale transit routes, significantly outperforming traditional methods on real-world data.

Contribution

The paper presents a novel end-to-end RL approach with a two-level reward structure for efficient transit network design on realistic city data.

Findings

Achieves 25.6% higher service rates than real-world networks.

Reduces wait times by 30.9% and improves bus utilization by 21%.

Outperforms heuristics with 68.8% higher route efficiency.

Abstract

Designing efficient transit route networks is an NP-hard problem with exponentially large solution spaces that traditionally relies on manual planning processes. We present an end-to-end reinforcement learning (RL) framework based on graph attention networks for sequential transit network construction. To address the long-horizon credit assignment challenge, we introduce a two-level reward structure combining incremental topological feedback with simulation-based terminal rewards. We evaluate our approach on a new real-world dataset from Bloomington, Indiana with topologically accurate road networks, census-derived demand, and existing transit routes. Our learned policies substantially outperform existing designs and traditional heuristics across two initialization schemes and two modal-split scenarios. Under high transit adoption with transit center initialization, our approach achieves 25.6% higher service rates, 30.9\% shorter wait times, and 21.0% better bus utilization compared to the real-world network. Under mixed-mode conditions with random initialization, it delivers 68.8% higher route efficiency than demand coverage heuristics and 5.9% lower travel times than shortest path construction. These results demonstrate that end-to-end RL can design transit networks that substantially outperform both human-designed systems and hand-crafted heuristics on realistic city-scale benchmarks.