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.
Investigation of Changes in Passenger Behavior Using Longitudinal Smart Card Data
Int. J. ITS Res.
International Journal of Intelligent Transportation Systems Research ; 19 , 1 ; 155-166
2021-04-01
12 pages
Article (Journal)
Electronic Resource
English
Longitudinal smart card data , K-means clustering , Passenger travel patterns , Public transport Engineering , Electrical Engineering , Automotive Engineering , Robotics and Automation , Computer Imaging, Vision, Pattern Recognition and Graphics , Civil Engineering , User Interfaces and Human Computer Interaction
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