Traffic zone division based on big data from mobile phone base stations

Call detail record (CDR) data from mobile communication carriers offer an emerging and promising source of information for analysis of traffic problems. To date, research on insights and information to be gleaned from CDR data for transportation analysis has been slow, and there has been little progress on development of specific applications. This paper proposes the traffic semantic concept to extract traffic commuters’ origins and destinations information from the mobile phone CDR data and then use the extracted data for traffic zone division. A K-means clustering method was used to classify a cell-area (the area covered by a base stations) and tag a certain land use category or traffic semantic attribute (such as working, residential, or urban road) based on four feature data (including real-time user volume, inflow, outflow, and incremental flow) extracted from the CDR data. By combining the geographic information of mobile phone base stations, the roadway network within Beijing’s Sixth Ring Road was divided into a total of 73 traffic zones using another K-means clustering algorithm. Additionally, the authors proposed a traffic zone attribute-index to measure tendency of traffic zones to be residential or working. The calculated attribute-index values of 73 traffic zones in Beijing were consistent with the actual traffic and land-use data. The case study demonstrates that effective traffic and travel data can be obtained from mobile phones as portable sensors and base stations as fixed sensors, providing an opportunity to improve the analysis of complex travel patterns and behaviors for travel demand modeling and transportation planning.

• Record URL:
  https://doi.org/10.1016/j.trc.2015.06.007
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X15002223
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Dong, Honghui
  • Wu, Mingchao
  • Ding, Xiaoqing
  • Chu, Lianyu
  • Jia, Limin
  • Qin, Yong
  • Zhou, Xuesong
• Publication Date: 2015-9

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Maps; References; Tables;
• Pagination: pp 278-291
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 58, Part B
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Algorithms; Case studies; Cellular telephones; Cluster analysis; Commuters; Data mining; Origin and destination; Traffic data; Travel demand; Travel patterns
• Uncontrolled Terms: Traffic zones
• Geographic Terms: Beijing (China)
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01576788
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 25 2015 4:28PM