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:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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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
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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: May 1 2017 9:37AM