An Accident Prediction Model for Urban Road Networks

For several years now increasingly analytical prediction models have been developed that are able to correlate accident frequency with infrastructure characteristics to support the planning and design of measures for enhancing road safety. The models developed so far, though useful in the context for which they have been calibrated, are limited by the fact that they are not transferable to other contexts because of different traffic regulations. The present work aims to develop a predictive model for urban roads that is able to estimate the number of accidents for three situations in an urban road network, a roundabout, a three- or four-way junction, and a straight stretch of road. The models constructed are based on Poisson's and negative binomial algorithms and can be readily applied for accident prediction or identification of black spots.

• Record URL:
  • https://doi.org/10.1080/19439962.2016.1268659
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/19439962
• Supplemental Notes:
  • © 2018 Taylor & Francis Group, LLC and The University of Tennessee 2018.
• Authors:
  • Gianfranco, Fancello
  • Soddu, Stefano
  • Fadda, Paolo
• Publication Date: 2018-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 387-405
• Serial:
  • Journal of Transportation Safety & Security
  • Volume: 10
  • Issue Number: 4
  • Publisher: Taylor & Francis
  • ISSN: 1943-9962
  • EISSN: 1943-9970
  • Serial URL: http://www.tandfonline.com/loi/utss20#.U2qTJPldWCm

Subject/Index Terms

• TRT Terms: Crash rates; High risk locations; Highway safety; Infrastructure; Mathematical prediction; Roundabouts; Urban highways
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01677529
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 8 2018 9:07AM