Abstract
The daily travel patterns (DTPs) present short-term and timely characteristics of the users’ travel behaviour, and they are helpful for subway planners to better understand the travel choices and regularity of subway users (SUs) in details. While several well-known subway travel patterns have been detected, such as commuting modes and shopping modes, specific features of many patterns are still confused or omitted. Now, based on the automatic fare collection (AFC) system, a data-mining procedure to recognize DTPs of all SUs has become possible and effective. In this study, DTPs are identified by the station sequences (SSs), which are modelled from smart card transaction data of the AFC system. The data-mining procedure is applied to a large weekly sample from the Beijing Subway to understand DTPs. The results show that more than 93% SUs of the Beijing Subway travel in 7 DTPs, which are remarkably stable in share and distribution. Different DTPs have their own unique characteristics in terms of time distribution, activity duration and repeatability, which provide a wealth of information to calibrate different types of users and characterize their travel patterns.
Highlights
Urban rail transit has become an indispensable option for daily travel in China, especially for commuters in the metropolises such as Beijing and Shanghai [1, 2]
A data mining methodology is proposed for analysing the daily travel pattern features observed by smart card data
Travel patterns (DTPs) are reflected by station sequence (SS), which is calculated from station code (SC)
Summary
Urban rail transit has become an indispensable option for daily travel in China, especially for commuters in the metropolises such as Beijing and Shanghai [1, 2]. The time and space regularity of different travel types are detected, the daily travel characteristics of subway users are still not clear enough for subway operation optimization and line network planning. While these works highlight the potential of clustering algorithm to classify the travel patterns, the approaches are still limited with the clustering variables which might ignore or be affected tremendously by some abnormal data, as well as the number of clusters [17]. The paper is concluded by summarizing the research findings and suggesting directions for future research
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
