Research On Analysis Of Urban Rail Transit Passenger Travel Path Based On Timetable

The increasing passenger flow volume and complexity of urban railway system in China have proposed higher demand of construction level and operation management quality.However,under the background of seamless transfer and multiline,the passenger travel plan and route are full of uncertainty.The analysis of passenger travel plan can not only serve as the basis for the operator distribution,but also calculate the real-time cross-section passenger flow data in the system accurately,which can provide strong support for making the reasonable station operation plan and the passenger organization.In this paper,based on the mixed integer linear characteristics of the effective path search model and the threshold value of path length,an iterative algorithm is designed to search the effective path in the spatiotemporal topological network of urban railway system.In view of the Origins-Destinations that include multiple efficient paths,to reduce the interference which generated by mixed multipath travel time distribution,split each efficient path into several single Origins-Destinations path,by the method of AFC data matching with train schedules,solve the corresponding time of station in turn,obtain the travel time of effective paths simply.The fitting results show that the method is reliable.Based on the fuzzy c-means clustering algorithm,take advantage of pulse phenomenon in passenger outbound process,the two-dimension target data set consist of outbound timedelay matrix and passenger travel time,set passenger travel plan as the clustering target,structure passenger travel plan infer method based on the timetable(FCM-TP),so that the discrete passenger data will be assigned to the specific train,and we can acquire more elaborate distribution of passenger flow.Take the rush hour of Chengdu railway system as the experiment scene,the passenger retention behavior was fully considered,compared the cluster center with the effective path theory node and deduced the specific travel plan of each passenger.In the end,the rationality of the algorithm solution was verified by the authentic passenger flow data of the section.Compared with the existing studies,the calculation method of standard travel time in this paper is more practical and we incorporate the rules of passengers’ departure time into the research innovatively.The experiment results show that the FCM-TP algorithm proposed in this paper can effectively distinguish the passenger travel plans with similar travel time,it also has a good clustering effect on the passenger retention time.