Abstract
Characterizing individual mobility is critical to understand urban dynamics and to develop high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal mobility patterns. However, due to the limitations of the underlying datasets, these studies could not investigate how mobility patterns differ over user characteristics among demographic groups. In this study, we analyzed a large-scale Automatic Fare Collection (AFC) dataset of the transit system of Seoul, South Korea and investigated how mobility patterns vary over user characteristics and modal preferences. We identified users’ commuting locations and estimated the statistical distributions required to characterize their spatio-temporal mobility patterns. Our findings show the heterogeneity of mobility patterns across demographic user groups. This result will significantly impact future mobility models based on trajectory datasets.
Highlights
Characterizing individual mobility is critical to understand the dynamics of cities and to develop high-resolution mobility models
A common problem of most trajectory datasets is that they do not have the socio-demographic features of the users and their modal preferences and limit our understanding on how mobility patterns vary over these features
Understanding how mobility patterns vary over these features is important to researchers since mobility is critical to vulnerable population such as seniors and disabled groups, and their mobility even relates to the quality of life
Summary
Characterizing individual mobility is critical to understand the dynamics of cities and to develop high-resolution mobility models. To understand the spatio-temporal patterns of human mobility, previous studies analyzed the displacements between two locations and the stay times at visited locations. These studies relied on individual trajectories generated by mobile phone call records, social-media posts, WiFi signals, and taxi-cab. The advantages of AFC data, such as a massive amount of records, the large scale of the study area and time period, and a large group of users, make it a great data source for understanding and analyzing human-mobility patterns.
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