Research On Timetable Optimization Of Last Train In Urban Rail Based On Delay And Transfer Satisfaction

With the rapid development of technology,the punctuality rate of urban rail transit has been continuously improved.However,unexpected incidents still occur and often cause train delays.Under the operation of networked rail transit,train delays not only affect the travel experience of passengers on the delayed routes,but also reduce the operating efficiency and passenger satisfaction of other lines.Especially for the last train,train delays can reduce passenger transfer satisfaction and rail transit accessibility,either lightly or severely.Optimizing the train timetable is an effective solution to adjust train delays.In the event of a delay in the last train,timely and effective delay adjustment measures should be taken to optimize and adjust the last train timetable,which can improve passenger transfer satisfaction,rail transit accessibility,and enterprise operating benefits.This article studies and builds an optimized model of the last train timetable for urban rail transit based on delay and transfer satisfaction from the perspective of passenger transfer satisfaction and operation,which has certain reference value for the operation,organization,and scheduling management of urban rail transit under the delay of the last train.Firstly,in this article,AFC card swipe data and rail operation data are used as the research basis,and a detailed introduction is given to the AFC card swipe data source and data processing methods of rail transit.The steps of passenger path assignment based on the Dijkstra algorithm for complete assignment with constraints are discussed.Factors affecting passenger walking speed within stations are also analyzed,and the probability density function of passenger walking time within stations is obtained.Taking into account the speed differences of passengers during transfer walking,an end-of-service passenger flow distribution model is constructed.Secondly,the passenger flow characteristics and operational characteristics of the last train are studied,and mathematical models are used to analyze the transfer connection of the last train.The causes,propagation characteristics,impacts,and commonly used adjustment measures for train delays are studied.Six principles for adjusting last train delays are summarized.Then,the transfer perception cost is introduced to quantitatively describe passenger transfer satisfaction,and the transfer perception costs of passengers for the three cases of catching up,transfer waiting,and transfer failure are analyzed in detail.For the problem of last train delay adjustment,a last train schedule optimization model is established from the perspectives of passenger overall transfer perception cost and operation,with the goals of minimizing the overall transfer perception cost of passengers and minimizing the total offset of the train schedule.The NSGA-II-DE algorithm is designed to solve the model.Finally,taking the local rail transit network composed of Chongqing Rail Transit Line 1,Line 3,and Line 6 as an example,simulation optimization is carried out using the proposed model and algorithm under the scenarios of single-line and multi-line last train delays.The results show that compared with before optimization,the optimized schedule reduces the overall transfer perception cost of passengers by an average of over 40%,up to 58.8%;reduces the average number of transfer failure passengers by over 78.6%,up to 100%,that is,all last train passengers successfully transfer;and reduces the total offset of the last train schedule by an average of over 24.1%,up to92%.The city rail transit last train schedule optimization model based on delay and transfer satisfaction constructed in this article has preliminarily solved the problem of last train schedule optimization adjustment under the last train delay of city rail transit and achieved good optimization effects in the application of single-line and multi-line last train delay scenarios of city rail transit networks,enriching to some extent the organizational and operational optimization of city rail transit networks.