ViFi-MobiScanner: Observe Human Mobility via Vehicular Internet Service

Exploring human mobility is essential for urban applications. To observe human mobility, various data-driven techniques based on different data sources, such as cell phone and transportation data, have been proposed. This paper investigates human mobility through the emerging vehicular Internet service on public bus system. The key idea is that if a passenger is using WiFi on the bus or his/her WiFi device is activated in the background, the authors know that the passenger is traveling on the bus. By fusing the network events generated by WiFi devices with the data from the automatic fare collection (AFC) system, and the bus GPS information, the authors exploit not only the origin but also the destination of a passenger. Based on this idea, they develop a novel system called ViFi-MobiScanner which consists of about 4, 800 mobile routers distributed in a city with 1, 992 KM2 urban area. They develop an ID matching algorithm that matches part of the users’ network identities and their smartcard identities anonymously. As a result, the authors have built a set of labeled samples with the reference of observation from smartcard data and use them to train a classifier to infer users mobility from their network activities. They evaluate ViFi-MobiScanner with both field tests and collected datasets associated with 168 million network events, 3.6 million trips, and 1.4 million users. The evaluation results show that ViFi-MobiScanner increases the observability on the passengers and trips by about 53.9% and 48.1% over the smartcard observations. ViFi-MobiScanner also helps to estimate the passengers’ destination that cannot be observed by current smartcard systems and the estimation can be accomplished in minutes. Thus it expands the observability of mobility in object, temporal and spatial dimensions and provides unique insights on human mobility at metropolitan scales.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2019.2956744
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Tu, Lai
  • Wang, Shuai
  • Zhang, Desheng
  • Zhang, Fan
  • He, Tian
• Publication Date: 2021-1

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 280-292
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 22
  • Issue Number: 1
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Buses; Data fusion; Data mining; Global Positioning System; Mobility; Origin and destination; Travel behavior
• Identifier Terms: WiFi services
• Geographic Terms: Shenzen (China)
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01766315
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Feb 28 2021 4:52PM