Public transportation systems often suffer from unexpected fluctuations in demand and disruptions, such as mechanical failures and medical emergencies. These fluctuations and disruptions lead to delays and overcrowding, which are detrimental to the passengers' experience and to the overall performance of the transit service. To proactively mitigate such events, many transit agencies station substitute (reserve) vehicles throughout their service areas, which they can dispatch to augment or replace vehicles on routes that suffer overcrowding or disruption. However, determining the optimal locations where substitute vehicles should be stationed is a challenging problem due to the inherent randomness of disruptions and due to the combinatorial nature of selecting locations across a city. In collaboration with the transit agency of Nashville, TN, we address this problem by introducing data-driven statistical and machine-learning models for forecasting disruptions and an effective randomized local-search algorithm for selecting locations where substitute vehicles are to be stationed. Our research demonstrates promising results in proactive disruption management, offering a practical and easily implementable solution for transit agencies to enhance the reliability of their services. Our results resonate beyond mere operational efficiency: by advancing proactive strategies, our approach fosters more resilient and accessible public transportation, contributing to equitable urban mobility and ultimately benefiting the communities that rely on public transportation the most.

翻译：公共交通系统常面临需求突发波动及中断事件，如机械故障和医疗紧急情况。这些波动和中断会导致延误和过度拥挤，损害乘客体验及公交服务的整体效能。为主动缓解此类事件，许多公交机构在其服务区域内设置备用（储备）车辆，以便在路线出现过度拥挤或中断时调度补充或替换车辆。然而，由于中断事件固有的随机性以及城市内选址的组合特性，确定备用车辆的最优驻点位置是一项具有挑战性的问题。通过与田纳西州纳什维尔公交机构合作，我们引入数据驱动的统计与机器学习模型来预测中断事件，并提出一种有效的随机局部搜索算法来选择备用车辆驻点位置。本研究表明，该方法在主动中断管理方面具有显著成效，可为公交机构提供一种实用且易于实施的解决方案，以提升服务可靠性。其意义不仅限于运营效率：通过推进主动策略，我们的方法有助于构建更具韧性和可及性的公共交通系统，促进公平的城市出行，最终惠及最依赖公共交通的社区。