A dynamic simulation model of passenger flow distribution on schedule-based rail transit networks wi

来源 :Journal of Traffic and Transportation Engineering(English Ed | 被引量 : 0次 | 上传用户:yanshileia001
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In a schedule-based rail transit system,passenger route choices are affected by train delays,and,consequently,the relevant passenger flow distribution of the network will differ from the normal state.In this paper,a passenger’s alternative choices,such as selecting another route,waiting,and switching to other transportation modes,and the corresponding influence mechanism are analyzed in detail.Given train time-space diagrams and the time-varying travel demands between the origin and destination(O-D),a dynamic simulation model of passenger flow distribution on schedule-based transit networks with train delays is proposed.Animation demonstration and statistical indices,including the passenger flow volume of each train and station,can be generated from simulation results.A numerical example is given to illustrate the application of the proposed model.Numerical results indicate that,compared with conventional methods,the proposed model performs better for a passenger flow distribution with train delays. In a schedule-based rail transit system, passenger route choices are affected by train delays, and,,,, the relevant passenger flow distribution of the network will differ from the normal state. In this paper, a passenger’s alternative choices, such as selecting another route, waiting, and switching to other transportation modes, and the corresponding influence mechanisms are analyzed in detail. Given train time-space diagrams and the time-varying travel demands between the origin and destination (OD), a dynamic simulation model of passenger flow distribution on schedule-based transit networks with train delays is proposed. Animation demonstration and statistical indices, including the passenger flow volume of each train and station, can be generated from simulation results. A numerical example is given to illustrate the application of the proposed model . Numerical result indicates that, compared with conventional methods, the proposed model performs better for a passenger flow distribution with train delays.
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