Roundabout accident prediction model: random-parameter negative binomial approach

Kamla, Jwan; Parry, Tony; Dawson, Andrew

Abstract

Roundabouts have been used widely on all road classes in the United Kingdom because they are considered safer than other types of intersections in general. The objective of this study was to examine geometric and traffic characteristics and their influences on the number of accidents. Data from each of 70 roundabouts (with 284 approaches) included all recorded vehicle accidents as well as geometric and traffic characteristics for the entire roundabout, within circulatory lanes, and at roundabout approaches. Resulting estimates were compared with those from random-parameter and fixed-parameter negative binomial count data models. The random-parameter results provided better goodness of fit than the fixed-parameter results, and more variables were found to be significant. Significant variables that influenced the number of accidents were total approach traffic, truck percentage, entry width, inscribed circle diameter, number of lanes, and presence of traffic signals.

Citation

Kamla, J., Parry, T., & Dawson, A. (2016). Roundabout accident prediction model: random-parameter negative binomial approach. Transportation Research Record, 2585(1), 11-19. https://doi.org/10.3141/2585-02

• Journal Article Type: Article
• Acceptance Date: Feb 25, 2016
• Online Publication Date: Oct 25, 2018
• Publication Date: Jan 1, 2016
• Deposit Date: Oct 14, 2016
• Publicly Available Date: Oct 14, 2016
• Journal: Transportation Research Record
• Electronic ISSN: 2169-4052
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 2585
• Issue: 1
• Pages: 11-19
• DOI: https://doi.org/10.3141/2585-02
• Keywords: Roundabout, accidents, random-parameters, fixed-parameters
• Public URL: https://nottingham-repository.worktribe.com/output/797660
• Publisher URL: http://trrjournalonline.trb.org/doi/10.3141/2585-02
• Related Public URLs: http://trrjournalonline.trb.org/
• Additional Information: Copyright National Academy of Sciences.
• Contract Date: Oct 14, 2016