Assigning passenger flows on a metro network plays an important role in passenger flow analysis that is the foundation of metro operation. Traditional transit assignment models are becoming increasingly complex and inefficient. These models may even not be valid in case of sudden changes in the timetable or disruptions in the metro system. We propose a methodology for assigning passenger flows on a metro network based on automatic fare collection (AFC) data and realized timetable. We find that the routes connecting a given origin and destination (O-D) pair are related to their observed travel times (OTTs) especially their pure travel times (PTTs) abstracted from AFC data combined with the realized timetable. A novel clustering algorithm is used to cluster trips between a given O-D pair based on PTTs/OTTs and complete the assignment. An initial application to categorical O-D pairs on the Shanghai metro system, which is one of the largest systems in the world, shows that the proposed methodology works well. Accompanying the initial application, an interesting approach is also provided for determining the theoretical maximum accuracy of the new assignment model.

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clustering algorithm methodology pttsotts observed travel times automatic fare collection afc data assigning passenger flows transit assignment models maximum accuracy shanghai metro system timetable categorical od pairs passenger flow analysis

Ling Hong,.Assigning Passenger Flows on a Metro Network Based on Automatic Fare Collection Data and Timetable. 2017 (),.

Ling Hong,"Assigning Passenger Flows on a Metro Network Based on Automatic Fare Collection Data and Timetable" 2017

Ling Hong,"Assigning Passenger Flows on a Metro Network Based on Automatic Fare Collection Data and Timetable"