Deriving Transportation Mode Shares on Urban Freeways Based on Mobile Phone Data
An innovative method is presented in this paper to derive the transportation mode shares on urban freeways using mobile-phone trajectory information. It consists of two major parts: offline learning and online inference. The offline learning first extracts the temporal feature from the mobile-phone trajectories. By comparing to the existed link volumes, the inference parameters are calibrated through the offline learning process. The online inference determines the transportation modes for each individual mobile phone users in a real-time manner. The methodology was tested via a case study designed for both the offline learning and online inference parts. The results show the great potential of using mobile-phone trajectory information as a means to estimating the transportation mode shares.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://itswc.confex.com/itswc/WC2011/webprogram/start.html
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Supplemental Notes:
- Abstract reprinted with permission from Intelligent Transportation Society of America.
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Corporate Authors:
1100 17th Street, NW, 12th Floor
Washington, DC United States 20036 -
Authors:
- Zhang, Yi
- Wan, Xia
- Qin, Xiao
- Ran, Bin
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Conference:
- 18th ITS World Congress
- Location: Orlando Florida, United States
- Date: 2011-10-16 to 2011-10-20
- Publication Date: 2011
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 16p
- Monograph Title: 18th ITS World Congress, Orlando, 2011. Proceedings
Subject/Index Terms
- TRT Terms: Case studies; Cellular telephones; Data collection; Freeways; Mobile telephones; Modal split; Traffic data; Traffic estimation; Urban areas; Wireless communication systems
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01487115
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 2 2013 8:28AM