Bus rapid transit (BRT) is a high-capacity public transport system that typically operates along urban transit corridors with dense travel demand. Maintaining the efficiency and stability of the BRT is paramount for daily transport operations. Owing to the difficulty in ensuring an exclusive right-of-way along the entire route, stochastic congestion events may occur resulting from road segments without dedicated BRT lanes. This may lead to volatility in travel time and resulting in passenger stranding, determined as common-case disruptions in this study. These common-case disruptions frequently occur in the daily operation of oversaturated BRT routes. To manage and mitigate their negative impacts, a novel timetabling problem for enhancing the resilience of a BRT system was proposed to assess the ability of the system to withstand and recover from these disruptions. We formulated the problem as a two-stage stochastic mixed-integer optimization model and designed an exact algorithm based on a tailored integer L-shaped method. We then analyzed the structural properties of our model and developed several acceleration techniques to further improve the efficiency of the algorithm. The computational results show that the proposed algorithm outperforms the commercial solver in large-scale instances and can provide near-optimal solutions when the commercial solver is invalid. Besides, external comparisons with dynamic programming also demonstrate the superiority of the proposed algorithm in solution efficiency. Compared with the benchmark timetabling problem, which aims to reduce passenger waiting time, the proposed method can efficiently reduce the duration of the oversaturation period by 35.3%.

中文翻译：

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管理 BRT 走廊的过饱和：一种使用定制整数 L 形算法来增强恢复力的时间表的新方法


快速公交 (BRT) 是一种大容量公共交通系统，通常沿着出行需求密集的城市交通走廊运行。保持 BRT 的效率和稳定性对于日常交通运营至关重要。由于全线难以保证专属路权，没有BRT专用车道的路段可能会出现随机拥堵事件。这可能会导致旅行时间波动并导致乘客滞留，在本研究中被确定为常见情况的中断。这些常见的中断情况经常发生在过度饱和的 BRT 路线的日常运营中。为了管理和减轻其负面影响，提出了一种增强 BRT 系统弹性的新时间表问题，以评估系统承受这些中断并从中恢复的能力。我们将该问题表述为两阶段随机混合整数优化模型，并设计了基于定制整数 L 形方法的精确算法。然后，我们分析了模型的结构特性，并开发了几种加速技术来进一步提高算法的效率。计算结果表明，该算法在大规模实例中优于商业求解器，并且在商业求解器无效时能够提供接近最优的解决方案。此外，与动态规划的外部比较也证明了该算法在求解效率上的优越性。与旨在减少乘客等待时间的基准时刻表问题相比，该方法可以有效地将过饱和期的持续时间减少35.3%。