Calibrating a Real-Time Traffic Crash-Prediction Model Using Archived Weather and ITS Traffic Data

In this paper, the authors present a crash-likelihood prediction model using real time traffic flow variables and rain data potentially associated with crash occurrences on Interstate 4 in the Central Florida area. Using online loop and rain data, the model is used to identify high crash potential in real time. A weather model that determines a rain index based on rain readings in proximity of the freeway is established using principal component analysis and logistic regression. The crash potential, based on traffic loop data and the rain index, is then modeled using a matched case-control logit model.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Authors:
  • Abdel-Aty, Mohamed A
  • Pemmanaboina, Rajashekar
• Publication Date: 2006-6

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 167-174
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 7
  • Issue Number: 2
  • 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: Crashes; Driver information systems; Estimation theory; Forecasting; Intelligent transportation systems; Logits; Probability; Rain; Rainfall; Regression analysis; Traffic safety
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors; I80: Accident Studies;

Filing Info

• Accession Number: 01055872
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: BTRIS, TRIS
• Created Date: Aug 20 2007 5:39PM