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.

通过引入数据驱动的统计与机器学习模型以及有效的随机化局部搜索算法，我们的研究在公共交通的积极干预和管理方面取得了有希望的结果，为交通管理机构提供了一种实用且易于实施的解决方案，以增强其服务的可靠性，进而促进更具弹性和可获得性的公共交通，使最依赖公共交通的社区受益。

公交运输服务的预测和应对中断

Forecasting and Mitigating Disruptions in Public Bus Transit Services

Integer programs provide a powerful abstraction for representing a wide range
of real-world scheduling problems. Despite their ability to model general
scheduling problems, solving large-scale integer programs (IP) remains a
computational challenge in practice. The incorporation of more complex
objectives such as robustness to disruptions further exacerbates the
computational challenge. We present NICE (Neural network IP Coefficient
Extraction), a novel technique that combines reinforcement learning and integer
programming to tackle the problem of robust scheduling. More specifically, NICE
uses reinforcement learning to approximately represent complex objectives in an
integer programming formulation. We use NICE to determine assignments of pilots
to a flight crew schedule so as to reduce the impact of disruptions. We compare
NICE with (1) a baseline integer programming formulation that produces a
feasible crew schedule, and (2) a robust integer programming formulation that
explicitly tries to minimize the impact of disruptions. Our experiments show
that, across a variety of scenarios, NICE produces schedules resulting in 33%
to 48% fewer disruptions than the baseline formulation. Moreover, in more
severely constrained scheduling scenarios in which the robust integer program
fails to produce a schedule within 90 minutes, NICE is able to build robust
schedules in less than 2 seconds on average.

本文提出了一种结合深度强化学习和整数规划的技术 ——NICE，用于解决飞行员航班表调度中的鲁棒性问题，实验结果表明，相较于基线方案和鲁棒整数规划，NICE 能够更有效地减少调度中不良因素所带来的影响。