City-Wide Traffic Flow Estimation From a Limited Number of Low-Quality Cameras

The authors present a new approach to lightweight intelligent transportation systems. Their approach does not rely on traditional expensive infrastructures, but rather on advanced machine learning algorithms. It takes images from traffic cameras at a limited number of locations and estimates the traffic over the entire road network. The authors' approach features two main algorithms. The first is a probabilistic vehicle counting algorithm from low-quality images that falls into the category of unsupervised learning. The other is a network inference algorithm based on an inverse Markov chain formulation that infers the traffic at arbitrary links from a limited number of observations. The authors evaluated their approach on two different traffic data sets, one acquired in Nairobi, Kenya, and the other in Kyoto, Japan.

• Record URL:
  https://doi.org/10.1109/TITS.2016.2597160
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Authors:
  • Idé, Tsuyoshi
  • Katsuki, Takayuki
  • Morimura, Tetsuro
  • Morris, Robert
• Publication Date: 2017-4

Language

• English

Media Info

• Media Type: Digital/other
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 950-959
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 18
  • Issue Number: 4
  • 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: Algorithms; Bayes' theorem; Markov processes; Traffic counting; Traffic estimation; Traffic flow; Urban transportation; Vehicle detectors; Video cameras
• Geographic Terms: Kyoto (Japan); Nairobi (Kenya)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01634026
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Mar 30 2017 3:56PM