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.

翻译：设计能够满足人们出行需求的公共交通网络对于减少道路上个体车辆数量、进而降低污染与拥堵至关重要。因此，城市可持续性与高效的公共交通系统紧密相连。当前交通网络设计方法通常以优化广义成本（即包含运营商与用户成本的单一数值）为目标。由于我们将公共交通质量定义为满足出行需求的能力，本研究转而聚焦于公共交通可达性，即通过公共交通到达周边兴趣点的便利程度。公共交通可达性在城市区域中通常分布不均：郊区普遍存在公共交通可达性不足的问题，导致当地居民被迫依赖私家车。因此，我们致力于通过公交线路设计来最小化可达性地理分布的不平等性。本研究结合了先进的消息传递神经网络与强化学习方法，并以简化表征蒙特利尔城市的案例验证了该方法相较于传统交通网络设计中常用的元启发式算法的优越性。