Random parameter models of interstate crash frequencies by severity, number of vehicles involved, collision and location type

• Published in: Accident analysis and prevention 2013-10, Vol.59, p.309-318
• Main Authors: Venkataraman, Narayan, Ulfarsson, Gudmundur F., Shankar, Venky N.
• Format: Article
• Language: English
• Subjects: Accidents, Traffic - mortality; Accidents, Traffic - statistics & numerical data; Automobiles - statistics & numerical data; Biological and medical sciences; Crash frequency aggregations; Crash severities; Crashes; Environment Design - statistics & numerical data; Heterogeneity; Humans; Illumination; Injuries; Interchange; Lighting; Mathematical models; Medical sciences; Miscellaneous; Models, Statistical; Prevention and actions; Public health. Hygiene; Public health. Hygiene-occupational medicine; Random parameters; Roads; Roadway geometrics; Traffic engineering; Traffic flow; Washington
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2013.06.021

•Random parameter negative binomial (RPNB) traffic crash frequency models.•Significant improvement in fit when using RPNB compared to fixed parameters.•Both-sides lighting generally leads to a safety improvement over one-side lighting.•Roadway cross-section effects have random effects only in interchange type models.•Road segment-specific insights into crash frequency lead to improved design policy. A nine-year (1999–2007) continuous panel of crash histories on interstates in Washington State, USA, was used to estimate random parameter negative binomial (RPNB) models for various aggregations of crashes. A total of 21 different models were assessed in terms of four ways to aggregate crashes, by: (a) severity, (b) number of vehicles involved, (c) crash type, and by (d) location characteristics. The models within these aggregations include specifications for all severities (property damage only, possible injury, evident injury, disabling injury, and fatality), number of vehicles involved (one-vehicle to five-or-more-vehicle), crash type (sideswipe, same direction, overturn, head-on, fixed object, rear-end, and other), and location types (urban interchange, rural interchange, urban non-interchange, rural non-interchange). A total of 1153 directional road segments comprising of the seven Washington State interstates were analyzed, yielding statistical models of crash frequency based on 10,377 observations. These results suggest that in general there was a significant improvement in log-likelihood when using RPNB compared to a fixed parameter negative binomial baseline model. Heterogeneity effects are most noticeable for lighting type, road curvature, and traffic volume (ADT). Median lighting or right-side lighting are linked to increased crash frequencies in many models for more than half of the road segments compared to both-sides lighting. Both-sides lighting thereby appears to generally lead to a safety improvement. Traffic volume has a random parameter but the effect is always toward increasing crash frequencies as expected. However that the effect is random shows that the effect of traffic volume on crash frequency is complex and varies by road segment. The number of lanes has a random parameter effect only in the interchange type models. The results show that road segment-specific insights into crash frequency occurrence can lead to improved design policy and project prioritization.