基于快速通勤需求的地铁列车跨站停车方案优化

Optimization of Skip-Stop Operation for Metro System Based on Rapid Commuting Demand

摘要:

AB跨站停车方案可以满足高峰时段地铁通勤线路场景下的乘客快速出行需求。在充分考虑乘客换乘便利性和候车安全性的基础上，以最小化乘客总旅行时间并兼顾换乘乘客的公平性影响为目标，基于出行过程和换乘类别建立了地铁AB跨站停车0-1整数规划模型，并设计了高效的变邻域搜索算法，最后以上海地铁11号线为例验证了模型和算法的有效性。结果表明：变邻域搜索算法相较于遗传算法可在短时间内搜索得到较优解，能够很好地适用于AB跨站停车优化模型；AB跨站停车方案人均旅行时间可节省2.91 min，考虑人工经验策略的AB跨站停车方案人均旅行时间可节省2.12 min，且换乘乘客数量可减少41.18%；通过灵敏度分析可以得出，换乘站候车时间惩罚系数、列车始发间隔时间和最大AB类车站间隔是影响优化结果的关键因素。

Abstract:

The AB skip-stop operation can meet the rapid travel demand of passengers in the scenario of commuting metro lines during peak hours. Considering passenger transfer convenience and waiting safety， a 0-1 integer programming model of AB skip-stop operation for the metro system was developed based on travel process and transfer type， to minimize the total travel time of passengers while considering the fairness of transfer passengers. Additionally， an efficient variable neighborhood search algorithm was designed to solve this model. Taking Shanghai Metro Line 11 as an example， the effectiveness of the model and algorithm was verified. The results show that the variable neighborhood search algorithm provides better solutions in a short time compared to the genetic algorithm and can be effectively applied to the optimization model of the AB skip-stop operation. Under the AB skip-stop operation， the average travel time of passengers is reduced by 2.91 minutes. When considering the artificial experience strategy under the AB skip-stop operation， the average travel time is reduced by 2.12 minutes， and the number of transfer passengers decreases by 41.18 %. Sensitivity analysis reveals that the penalty coefficient for transfer station waiting time， train departure interval， and maximum AB station interval are the key factors affecting the optimization results.