Estimation method for railway passengers’ train choice behavior with smart card transaction data

Smart card systems have become the predominant method of collecting public transport fares in Japan. Transaction data obtained through smart cards have resulted in a large amount of archived information on how passengers use public transportation. The data have the potential to be used for modeling passenger behavior and demand for public transportation. This study focused on train choices made by railway passengers. If each passenger’s train choice can be identified over a long period of time, this information would be useful for improving the customer relationship management of the railway company and for improving train timetables. The aim of this study was to develop a methodology for estimating which train is boarded by each smart card holder. This paper presents a methodology and an algorithm for estimation using long-term transaction data. To validate the computation time and accuracy of the estimation, an empirical analysis is carried out using actual transaction data provided by a railway company in Japan. The results show that the proposed method is capable of estimating passenger usage patterns from smart card transaction data collected over a long time period.     

How do passengers use travel time? A case study of Shanghai–Nanjing high speed rail

Traditional travel behavior theory regards travel time as a waste. Recent studies suggest that it carries a positive utility, among other reasons for the benefit of the activities conducted while traveling. However, most studies of travel time use have focused on conventional trains in developed countries. Few have systematically examined the permeation of information and communication technology (ICT) into travel time use and the correlates of activity participation in developing countries, particularly on high speed rail (HSR). Using a survey conducted on the Shanghai–Nanjing corridor (N = 901), this study examines how HSR passengers use their travel time and explores the correlates of the different types of activities of business and non-business travelers, respectively, through multivariate probit models. We found that 96% of the respondents use ICT during their HSR journey and that most passengers spend some of their travel time on work-related activities. Moreover, items carried and advance planning as well as work-related travel attributes contribute significantly to activity participation. However, the factors affecting time use of business and non-business travelers differ. HSR service design should facilitate passenger engagement in various activities and improvement of their travel experience. A stable internet connection, adequate power sockets, and a noise-free environment will promote both work and leisure activities on the HSR.