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

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.