需求不确定下公共交通中断期间的鲁棒路径推荐
Robust Path Recommendations During Public Transit Disruptions Under Demand Uncertainty
AI总结：
针对公共交通中断时的乘客路径引导需求，本文提出考虑需求不确定性的鲁棒路径推荐模型，通过仿真近似与鲁棒优化求解，在CTA真实场景中有效降低系统及事故线路乘客出行时间，最坏需求场景下提升更明显。
摘要：
当公共交通系统发生重大服务中断时，乘客通常需要引导来寻找替代路径。本文提出一种路径推荐模型，以缓解公共交通中断期间的拥堵问题。模型为不同出发地、目的地和出发时间的乘客推荐不同路径，从而最小化系统总出行时间。我们将路径推荐问题建模为需求信息不确定的最优流问题。为解决容量约束导致出行时间缺乏解析表达式的问题，我们提出一种基于仿真的一阶近似方法，将原问题转化为线性规划问题。需求中的不确定性采用鲁棒优化进行建模，以保护路径推荐策略免受估计不准确的影响。我们以芝加哥交通局（CTA）系统的真实轨道交通中断场景作为案例研究。结果表明，即使不考虑不确定性，标称模型也能将系统出行时间降低9.1%（与现状相比），且性能优于基于容量的基准路径推荐方法。事故线路上的乘客（即接受推荐的乘客）的平均出行时间降幅更大（与现状相比降低20.6%）。纳入需求不确定性后，鲁棒模型可进一步降低系统出行时间。最优鲁棒模型与标称模型相比，可将事故线路乘客的平均出行时间降低2.91%。当实际需求模式接近最坏情况需求时，鲁棒模型的改进效果更为显著。
英文摘要：
When there are significant service disruptions in public transit systems, passengers usually need guidance to find alternative paths. This paper proposes a path recommendation model to mitigate congestion during public transit disruptions. Passengers with different origins, destinations, and departure times are recommended with different paths such that the system travel time is minimized. We model the path recommendation problem as an optimal flow problem with uncertain demand information. To tackle the lack of analytical formulation of travel times due to capacity constraints, we propose a simulation-based first-order approximation to transform the original problem into a linear program. Uncertainties in demand are modeled using robust optimization to protect the path recommendation strategies against inaccurate estimates. A real-world rail disruption scenario in the Chicago Transit Authority (CTA) system is used as a case study. Results show that even without considering uncertainty, the nominal model can reduce the system travel time by 9.1% (compared to the status quo), and outperforms the benchmark capacity-based path recommendation. The average travel time of passengers in the incident line (i.e., passengers receiving recommendations) is reduced more (-20.6% compared to the status quo). After incorporating the demand uncertainty, the robust model can further reduce system travel times. The best robust model can decrease the average travel time of incident-line passengers by 2.91% compared to the nominal model. The improvement of robust models is more prominent when the actual demand pattern is close to the worst-case demand.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
