Abstract
The study on individual mobility patterns supports our better understanding of spatiotemporal characteristics of people’s travel behavior and social activities. The mobile phone GPS data are advantageous due to the large size of their data coverage. This paper aims to identify individual activity anchor places and to analyze related patterns based on the GPS data collected from thousands of mobile phone users over four months in Greater Paris, France. We propose a method to refine the identification of home and secondary activities. Based on this, the mobility spatial characteristics are aggregated by applying a three-stage clustering method. As a consequence, the clusters of activity types, the daily mobility patterns (day types), and the user groups with similar daily mobility patterns are obtained stage by stage. This allows us to analyze the obtained clusters in a cascading maneuver by three different levels: activity level, day level, and individual level. Inversely, the mobility characteristics per user group are interpreted with respect to the interpretation of day types and then activity types. From the interpretable clusters, it is facilitated for us to find the daily mobility differences by user groups across weekdays and weekends, transport modes, as well as the mobility variability over the study period.
Published Version
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