Collaborative Optimization of Train Timetabling and Train Capacity Allocation for Large Passenger Flow Scenario
To alleviate platform congestion at large passenger flow stations and their downstream stations on urban rail transit commuter lines,and reduce the crowding level and safety risks,this paper systematically optimizes the allocation of train capacity resources from temporal and spatial levels.By considering time-varying passenger demand and the train carriage reservation strategy,a collaborative optimization method for train timetabling and train capacity allocation problems is proposed.Specifically,decision variables related to train departure time,number of reserved train carriages,and passenger assignment plan are introduced and an integer linear programming model for the train timetabling and train capacity allocation problem is formulated,to minimize the operational cost of train carriage reservation and the maximum number of waiting passengers on platforms.Among them,the passenger assignment constraints formulated by the Big-M method obey the first-in-first-out principle in time and space levels.To validate the effectiveness of the constructed model,four sets of numerical experiments are implemented by the Gurobi solver directly.The results show that,compared to planned train timetable and two single optimization strategies,the collaborative optimization method can significantly reduce the maximum number of waiting passengers by approximately 60%,52%,and 31%,and reduce the total passenger waiting time by about 29%,17%,and 29%,respectively.That is,the collaborative optimization method can balance the temporal and spatial distribution of the urban rail train capacity and effectively mitigate the risk of passenger crowding at platforms.
NETL
NSTL
万方数据
维普
