Random Forest Model for Trip End Identification Using Cellular Phone and Points of Interest Data

Cellular phone data has been proven to be valuable in the analysis of residents’ travel patterns. Existing studies mostly identify the trip ends through rule-based or clustering algorithms. These methods largely depend on subjective experience and users’ communication behaviors. Moreover, limited by privacy policy, the accuracy of these methods is difficult to assess. In this paper, points of interest data is applied to supplement cellular phone data’s missing information generated by users’ behaviors. Specifically, a random forest model for trip end identification is proposed using multi-dimensional attributes. A field data acquisition test is designed and conducted with communication operators to implement synchronized cellular phone data and real trip information collection. The proposed identification approach is empirically evaluated with real trip information. Results show that the overall trip end detection precision and recall reach 95.2% and 88.7% with an average distance error of 269?m, and the time errors of the trip ends are less than 10?min. Compared with the rule-based approach, clustering algorithm, naive Bayes method, and support vector machine, the proposed method has better performance in accuracy and consistency.

• Record URL:
  https://doi.org/10.1177/03611981211031537
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • Yanchen Wang https://orcid.org/0000-0002-1215-2277 © National Academy of Sciences: Transportation Research Board 2021.
• Authors:
  • Yang, Fei
  • Wang, Yanchen
  • 0000-0002-1215-2277
  • Jin, Peter J
  • 0000-0002-7688-3730
  • Li, Dingbang
  • Yao, Zhenxing
  • 0000-0001-8428-9080
• Publication Date: 2021-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 454-466
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2675
  • Issue Number: 7
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Cellular telephones; Data analysis; Decision trees; Location data; Trip length
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01779138
• Record Type: Publication
• Files: TRIS, TRB, ATRI
• Created Date: Aug 17 2021 10:40AM