Data-Driven Intelligent Transportation Systems: A Survey

For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS): a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS. Future research directions for the development of D2ITS is also presented.

• Record URL:
  https://doi.org/10.1109/TITS.2011.2158001
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Authors:
  • Zhang, Junping
  • Wang, Fei-Yue
  • Wang, Kunfeng
  • Lin, Wei-Hua
  • Xu, Xin
  • Chen, Cheng
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: pp 1624-1639
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 12
  • 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: Data fusion; Data mining; Detection and identification systems; Intelligent transportation systems; Machine learning; Machine vision; Performance measurement; Privacy; Visualization
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I71: Traffic Theory; I73: Traffic Control;

Filing Info

• Accession Number: 01359045
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: TLIB
• Created Date: Dec 16 2011 2:47PM