用神经网络与强化学习优化公交线路，缩小城乡出行便利差距。
Public Transport Network Design for Equality of Accessibility via Message Passing Neural Networks and Reinforcement Learning
- 结合消息传递神经网络与强化学习设计公交线路
- 在蒙特利尔简化模型中显著降低出行可达性不平等
- 适合城市规划者与交通政策研究者参考
设计能满足人们出行需求的公共交通（PT）网络，是减少道路上私家车数量、从而降低污染和拥堵的关键。城市可持续发展与高效的公共交通紧密相关。当前交通网络设计（TND）方法通常以综合成本（包括运营方与用户成本）为优化目标。但本文将公共交通质量定义为满足出行需求的能力，聚焦于公共交通可达性——即通过公交系统到达周边兴趣点的难易程度。现实中，可达性在城市区域分布不均，郊区普遍较差，导致居民被迫依赖私家车。因此，本文致力于设计公交线路，以最小化地理上可达性的不平等。方法上融合了前沿的消息传递神经网络（MPNN）与强化学习。在代表蒙特利尔的简化案例中，该方法相较于传统元启发式算法表现出更优效果。
原文摘要 · Abstract (English)
Designing Public Transport (PT) networks able to satisfy mobility needs of people is essential to reduce the number of individual vehicles on the road, and thus pollution and congestion. Urban sustainability is thus tightly coupled to an efficient PT. Current approaches on Transport Network Design (TND) generally aim to optimize generalized cost, i.e., a unique number including operator and users' costs. Since we intend quality of PT as the capability of satisfying mobility needs, we focus instead on PT accessibility, i.e., the ease of reaching surrounding points of interest via PT. PT accessibility is generally unequally distributed in urban regions: suburbs generally suffer from poor PT accessibility, which condemns residents therein to be dependent on their private cars. We thus tackle the problem of designing bus lines so as to minimize the inequality in the geographical distribution of accessibility. We combine state-of-the-art Message Passing Neural Networks (MPNN) and Reinforcement Learning. We show the efficacy of our method against metaheuristics (classically used in TND) in a use case representing in simplified terms the city of Montreal.
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