A Network Tomography Approach for Traffic Monitoring in Smart Cities

Traffic monitoring is a key enabler for several planning and management activities of a Smart City. However, traditional techniques are often not cost efficient, flexible, and scalable. This paper proposes an approach to traffic monitoring that does not rely on probe vehicles, nor requires vehicle localization through GPS. Conversely, it exploits just a limited number of cameras placed at road intersections to measure car end-to-end traveling times. The authors model the problem within the theoretical framework of network tomography, in order to infer the traveling times of all individual road segments in the road network. They specifically deal with the potential presence of noisy measurements, and the unpredictability of vehicles paths. Moreover, the authors address the issue of optimally placing the monitoring cameras in order to maximize coverage, while minimizing the inference error, and the overall cost. They provide extensive experimental assessment on the topology of downtown San Francisco, CA, USA, using real measurements obtained through the Google Maps APIs, and on realistic synthetic networks. Their approach provides a very low error in estimating the traveling times over 95% of all roads even when as few as 20% of road intersections are equipped with cameras.

• Record URL:
  • https://doi.org/10.1109/TITS.2018.2829086
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Zhang, Ruoxi
  • Newman, Sara
  • Ortolani, Marco
  • Silvestri, Simone
• Publication Date: 2018-7

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 2268-2278
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 19
  • Issue Number: 7
  • 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; Automatic data collection systems; Network analysis (Planning); Tomography; Traffic surveillance; Video imaging detectors
• Uncontrolled Terms: Smart cities
• Geographic Terms: San Francisco (California)
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01676891
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Jul 27 2018 1:24PM