# AlphaTransit：基于搜索学习的城市尺度公交线路设计框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-27 08:00
- AIHOT 分数：56
- AIHOT 链接：https://aihot.virxact.com/items/cmpvhtd13070ysl0z5seo9ebo
- 原文链接：https://arxiv.org/abs/2605.28730

## AI 摘要

AlphaTransit 是一个用于城市公交网络设计的搜索规划框架，旨在解决线路设计中因延迟反馈导致的路径交互欺骗性问题。该框架结合了蒙特卡洛树搜索（MCTS）与神经策略-价值网络：网络负责提出线路延伸方案并评估设计质量，搜索过程则利用这些预测进行决策，从而在构建线路时实现前瞻性。在 Bloomington TRNDP 基准测试中，AlphaTransit 在混合和全需求设置下分别达到了 54.6% 和 82.1% 的服务率。与不使用搜索的强化学习相比，服务率分别提升 9.9% 和 11.4%；与没有学习引导的 MCTS 相比，分别提升 2.5% 和 11.2%。该研究的代码与数据已开源。

## 正文

Designing a transit network requires many sequential route extension decisions, but their quality is often visible only after the full network is assembled. This delayed-feedback challenge lies at the heart of the Transit Route Network Design Problem (TRNDP), where route interactions can be deceptive: an extension that appears useful locally can create transfer bottlenecks, produce redundant overlap, or reduce overall throughput. To guide route construction under delayed simulator feedback, we introduce AlphaTransit, a search-based planning framework for cityscale bus network design. AlphaTransit couples Monte Carlo Tree Search (MCTS) with a neural policy-value network: the policy proposes route extensions, the value estimates downstream design quality, and search uses these predictions to refine each decision. This provides decision-time lookahead during route construction without running simulator rollouts inside the search tree. We evaluate AlphaTransit on a new Bloomington TRNDP benchmark with realistic road topology and censusderived demand, under mixed and full transit demand settings. In the Bloomington network, AlphaTransit attains the highest service rate in both demand settings, reaching 54.6% and 82.1%, respectively. Relative to reinforcement learning without search, these correspond to 9.9% and 11.4% service rate gains; relative to MCTS without learned guidance, they correspond to 2.5% and 11.2% gains. These results suggest that coupling learned guidance with MCTS is more effective than using either approach alone for transit network design. Our code and data are publicly available in https://github.com/poudel-bibek/AlphaTransit.