Learning to Design City-scale Transit Routes

• URL: http://arxiv.org/abs/2512.19767v1
• Date: Sun, 21 Dec 2025 12:48:53 GMT
• Title: Learning to Design City-scale Transit Routes
• Authors: Bibek Poudel, Weizi Li,
• Abstract summary: We present an end-to-end reinforcement learning framework based on graph attention networks for sequential transit network construction.<n>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.
• Score: 4.0801703556134425
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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