Investigation of Changes in Passenger Behavior Using Longitudinal Smart Card Data
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Investigation of Changes in Passenger Behavior Using Longitudinal Smart Card Data Rattanaporn Kaewkluengklom 1
&
Fumitaka Kurauchi 1 & Takenori Iwamoto 2
Received: 10 June 2020 / Revised: 19 August 2020 / Accepted: 6 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract To better understand long-term patterns of human mobility, this study examines changes in travel behavior at the individual level based on yearly activity profiles using 3 years of longitudinal smart card data collected in Shizuoka, Japan. We first characterize spatiotemporal patterns of railway usage by k-means clustering, and then investigate variation in cluster membership with time. For among passengers who remained active, regular commuters had similar travel patterns over the study period, whereas infrequent travelers significantly increased their use of the railway system. The evolution of cluster assignment is analyzed and discussed. Keywords Longitudinal smart card data . K-means clustering . Passenger travel patterns . Public transport
1 Introduction Deep analysis of passenger travel behavior allows transport authorities and planners to better understand travel supply and demand. Traditional travel demand modeling heavily emphasizes the number of passengers traveling between different origin–destination (OD) pairs, as well as passenger demand at stations. However, overall mobility patterns and habitual behavior are also of great interest, where better understanding of passenger travel patterns could improve decision-making and modeling simulations [1]. Cognizance of changes in passenger habits over time could aid predictions of the demand for public transport. In summary, studying mobility patterns may improve understanding of passenger behavior, such that public transport services can be optimized.
* Rattanaporn Kaewkluengklom [email protected] Fumitaka Kurauchi [email protected] Takenori Iwamoto [email protected] 1
Department of Civil Engineering, Gifu University, 1−1 Yanagido, Gifu 501-1193, Japan
2
Shizuoka Railway Co., Ltd., 1-1-1 Takajo, Shizuoka 420-0839, Japan
In line with this, this study examined patterns of public transport usage and yearly variability of travel behavior based on 3 years of longitudinal smart card data. We first determined the frequency of smart card transactions per year, where the identity (ID) number of each card was recorded to assess spatiotemporal patterns of public transport usage. Based on these yearly activity profiles, passengers were clustered by mobility pattern. We then investigated variability in passenger cluster membership over sequential 1-year periods to determine whether passengers changed their behavior between years. The outcomes of this study provide a deeper understanding of human mobility patterns and changes therein, which is important for improving travel forecasting models. The results may also be instructive for urban transport planners seeking to identify improvements that would encourage the use of public transport.
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