Research On Urban Rail Transit Resource Allocation Problem Based On The Transfer Coordination

As the rapid development of urban rail transit in the mainland of China, more and more people are attracted to choose this transportation means. The demand of transfer passenger flow between lines increases at a very fast speed. The core of transport capacity resources allocation is to determine various transport task, in which line planning and train timetabling play an important role. In order to achieve the goal of transfer coordination, an urban rail transit network on integrated routing scheme is built. The allocation of transport capacity resources are studied combining line planning and train timetabling in this issue.(1)By summarizing the transfer coordination, line planning and train timetabling for urban rail transit at home and abroad, it was found that allocation of transport capacity resources were more about a single line, there was little information about routing schemes based on transfer passenger flow.(2)Organization patterns based on transfer coordinating about urban rail transport were analyzed, also with the passenger flow characteristics and the element which affects passenger transfer. It mainly discussed the impact of different routing scheme. The objectives and measures of transfer coordination were summed up.(3)First, an urban rail transit network on integrated routing scheme was built. Then combining enterprise operating cost, operation efficiency and passenger travel cost, a model for transfer coordination of urban rail resource allocation was proposed. Harmony search algorithm was designed to solve the model.(4)The principles, methods and skills of setting the first and last train were introduced. Then the optimization model and solving steps were proposed in detail, which provided solutions and measures for further optimizing the train timetable.(5)Chongqing Rail Transit line1and line2was put as a case. Use proposed algorithm and ILOG CPLEX to sovel the model. The timetable of the first and last train was also optimizated The results verified the effectiveness of the model and algorithm.