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.