【摘要】 为缓解大型活动开始前大量乘客集中在活动举办地附近的地铁站出站而产生的站台拥堵问题，采用错开上下行列车的到站时刻和设计列车跳停策略的方法，建立以最小化出站乘客在站台的滞留时间、乘客出站时在站台的拥挤风险和列车跳停次数为目标的数学优化模型，对列车的运行时间和停站方案进行优化。此外，结合地铁运行图和大型活动的客流特点，设计迭代局部搜索算法求解。设计算法包含2种搜索可行邻域解的方法：一种是通过调整列车运行和停站时间搜索邻域解，另一种是通过调整停站方案搜索邻域解。以南昌某大型活动的数据为例，对模型和算法的效果进行验证，实例表明：设计的迭代局部搜索算法能快速得到高质量解，优化后站台上的人数最大值由2 951人降至1 006人，乘客的平均出站时间减少了94.7 s，减少明显，站台的拥挤风险降低了94%，错开列车到站时间和使列车跳停的策略能有效减少乘客的出站时间，缓解活动开始前活动现场附近车站的拥挤情况。更多还原

【Abstract】 To alleviate platform congestion caused by a large number of passengers exiting metro stations near event venues before the start of large-scale events, this study proposes a method that staggers the arrival times of trains in both directions and incorporates a skip-stop strategy. A mathematical optimization model is developed to minimize the platform dwell time of outbound passengers, the risk of crowding on the platform, and the number of skip-stop operations. Furthermore, an iterated local search algorithm is designed, taking into account the characteristics of passenger flow and the metro timetable during large-scale events. The algorithm explores feasible neighboring solutions in two ways: by adjusting train running and stopping times, and by modifying stop plans. Using data from a major event in Nanchang as a case study, the effectiveness of the model and algorithm is validated. The results show that the proposed iterated local search algorithm can quickly obtain high-quality solutions. After optimization, the maximum number of passengers on the platform decreased from 2,951 to 1,006, the average platform exit time for passengers was reduced by 94.7 seconds, and the platform crowding risk was reduced by 94%. The strategies of staggering train arrivals and implementing skip-stops can effectively reduce passenger exit times and alleviate congestion at stations near event venues before large-scale events begin.更多还原