Assessment of temporal stability in risk factors of crashes at horizontal curves on rural two-lane undivided highways

J Safety Res. 2021 Feb:76:205-217. doi: 10.1016/j.jsr.2020.12.003. Epub 2020 Dec 31.

Abstract

Introduction: Safety of horizontal curves on rural two-lane, two-way undivided roadways is not fully explored. This study investigates factors that impact injury severity of such crashes.

Method: To achieve the aim of this paper, issues associated with police-reported crash data such as unobserved heterogeneity and temporal stability need to be accounted for. Hence, a mixed logit model was estimated, while heterogeneity in means and variances is investigated by considering four injury severity outcomes for drivers: severe injury, moderate injury, possible injury, and no injury. Crash data for the period between 2011 and 2016 for crashes that occurred in the state of Oregon was analyzed. Temporal stability in factors determining the injury severity was investigated by identifying three time periods through splitting crash data into 2011-2012, 2013-2014, and 2015-2016.

Results: Despite some factors affecting injuries in all specified time periods, the values of the marginal effects showed relative differences. The estimation results revealed that some factors increased the risk of being involved in severe injury crashes, including head-on collisions, drunk drivers, failure to negotiate curves, older drivers, and exceeding the speed limits.

Conclusions: The hypothesis that attributes of injury severity are temporally stable is rejected. For example, young drivers (30 years old and younger) and middle-aged drivers were found to be temporally instable over time. Practical applications: The findings could help transportation authorities and safety professionals to enhance the safety of horizontal curves through appropriate and effective countermeasures.

Keywords: Heterogeneity in means and variances; Horizontal curve; Injury severity; Temporal stability; Two-lane undivided highways.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidents, Traffic / statistics & numerical data*
  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Female
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Oregon
  • Risk Factors
  • Safety / statistics & numerical data*
  • Young Adult