FUZZY LOGIC BASED ONLINE COLLISION PREDICTION SYSTEM FOR SIGNALIZED INTERSECTIONS

This paper proposes a conceptual framework of online collision prediction and avoidance system for signalized intersections. A fuzzy logic inference system is developed to perform online collision prediction, which is the core for implementation of intersection collision alerting/avoidance technologies. The risk is quantified by 3 fuzzy inputs to the model, time to collision, severity of the crash, and the local perception of the drivers. Perception and Severity Indices are integrated to reflect driver-vehicle system safety characteristics. An example at a hypothetical intersection is given to demonstrate the proposed methodology for the red light running vehicle. Discussion of the results and some extensions of this work are also provided.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/8879997300
• Corporate Authors:

  University Roma Tre

  Department of Sciences of Civil Engineering
  via Vito Volterra, 62
  Rome,   Italy  00146
• Authors:
  • Sun, D
  • Ukkusuri, S
  • Benekohal, R F
  • Waller, S T
  • Liu, Bowen
• Publication Date: 2004

Language

• English

Media Info

• Features: References;
• Pagination: p. 71-86
• Serial:
  • Advances in Transportation Studies
  • Volume: 3
  • Publisher: University Roma Tre
  • ISSN: 1824-5463
  • Serial URL: http://www.atsinternationaljournal.com/

Subject/Index Terms

• TRT Terms: Crash avoidance systems; Crash causes; Crash risk forecasting; Fuzzy logic; High risk locations; Red light running; Signalized intersections; Traffic crashes; Traffic engineering; Traffic safety
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors; I73: Traffic Control;

Filing Info

• Accession Number: 00976543
• Record Type: Publication
• ISBN: 8879997300
• Files: TRIS
• Created Date: Jul 26 2004 12:00AM