The Prediction Of Passenger Flow
Under Transport Disturbance Using
Accumulated Passenger Data
Author(s)
T. Kunimatsu & C. Hirai
Abstract
Whenever train disturbances occur, it is necessary for traffic operators to recover
the train timetable appropriately, considering the passenger flow. But it is
difficult to predict the flow in quantitative terms, because passengers may cancel
his or her travel, or detour to another rail line. In recent years, however, it has
become possible to obtain actual train operation time data stored in traffic
control systems, the number of passengers on board by means of load
compensating devices on rolling stock and passengers’ Origin-Destination data
collected with automatic ticket checkers. In this paper, we first propose a
visualization method of passengers’ flow. The method makes it easier for us to
understand features of passengers’ flow during traffic disturbances in
comparison with that of ordinary days. In the next step, we construct prediction
models for the number of passengers passing the section between two adjacent
stations. We implement multiple regression analysis using passenger’s flow data
and information on outline of disturbances on a commuter rail line in past 10
months. As a result, we get multiple regression formulas to predict increase or
decrease rates of the traffic volume in each section, with sufficient multiple
correlation coefficients about 0.75. Finally, we apply the formulas to other
disturbances, and find that they are reliable enough to support train rescheduling
operations.
Keywords: traffic disturbance, timetable, train rescheduling, passengers’ flow,
multiple regression analysis.
Keywords
traffic disturbance, timetable, train rescheduling, passengers’ flow,
multiple regression analysis.
Related Book
Other papers in this volume
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