Reconstructing human activities via coupling mobile phone data with location-based social networks

In the era of big data, the ubiquity of location-aware portable devices provides an unprecedented opportunity to understand inhabitants’ behavior and their interactions with the built environments. Among the widely used data resources, mobile phone data is the one passively collected and has the largest coverage in the population. However, mobile operators cannot pinpoint one user within meters, leading to the difficulties in activity inference. To that end, the authors propose a data analysis framework to identify user’s activity via coupling the mobile phone data with location-based social networks (LBSN) data. The two datasets are integrated into a Bayesian inference module, considering people’s circadian rhythms in both time and space. Specifically, the framework considers the pattern of arrival time to each type of facility and the spatial distribution of facilities. The former can be observed from the LBSN Data and the latter is provided by the points of interest (POIs) dataset. Taking Shanghai as an example, the authors reconstruct the activity chains of 1,000,000 active mobile phone users and analyze the temporal and spatial characteristics of each activity type. The authors assess the results with some official surveys and a real-world check-in dataset collected in Shanghai, indicating that the proposed method can capture and analyze human activities effectively. Next, the authors cluster users’ inferred activity chains with a topic model to understand the behavior of different groups of users. This data analysis framework provides an example of reconstructing and understanding the activity of the population at an urban scale with big data fusion.

• Record URL:
  • https://doi.org/10.1016/j.tbs.2023.100606
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2214367X23000571
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/2214367X
• Supplemental Notes:
  • © 2023 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Huang, Le
  • Xia, Fan
  • Chen, Hui
  • Hu, Bowen
  • Zhou, Xiao
  • Li, Chunxiao
  • Jin, Yaohui
  • Xu, Yanyan
• Publication Date: 2023-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 100606
• Serial:
  • Travel Behaviour and Society
  • Volume: 33
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2214-367X
  • Serial URL: http://www.sciencedirect.com/science/journal/2214367X

Subject/Index Terms

• TRT Terms: Activity choices; Location; Travel behavior
• Geographic Terms: Shanghai (China)
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01884872
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 13 2023 5:16PM