Exploring Individual Mobility Patterns Based on Geolocation Data from Mobile Phones

Understanding individual mobility patterns is significant for activity-based travel demand modeling. Data mining techniques can extract useful activity and travel information from large-scale data sources, such as mobile phone geolocation data. This paper aims at exploring individual mobility patterns from the samples of mobile phone users using a two-week geolocation dataset in the Paris region, France. After filtering the dataset, the authors propose techniques to identify individual stays and activity places. The typical activity places, such as the primary anchor place and the secondary place are also detected. The daily activity-travel program (i.e., timeline) is reconstructed with the detected activity places and the trips in-between. Based on the user-day timelines, a three-stage clustering method is proposed. In the method framework, the activity types are firstly identified by clustering analysis. In the second stage, the daily mobility patterns are obtained after clustering the daily mobility features. The activity-travel topologies are investigated statistically, which help to interpret the daily mobility patterns. In the last stage, at individual level, the authors analyze statistically the individual mobility patterns for all samples over 14 days, measured by the mean number of days for all kinds of the daily mobility patterns. All individual samples are classified into several groups where people have the similar travel behaviors. A kmeans++ algorithm is applied to obtain the appropriate number of patterns in each stage. Finally, the authors interpret the individual mobility patterns with statistical descriptions and reveal the home-based differences in spatial distribution for the grouped individuals.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 18p

Subject/Index Terms

Filing Info

  • Accession Number: 01764178
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-01547
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:21AM