Improving roadside design policies for safety enhancement using hazard-based duration modeling

Carlos Roque, Mohammad Jalayer

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Roadway departure (RwD) crashes, comprising run-off-road (ROR) and cross-median/centerline head-on collisions, are one of the most lethal crash types. Nationwide, from 2014 to 2016, annual RwD crashes accounted for 53% of all motor vehicle traffic fatalities. Several factors may cause a driver leave the travel lane, including an avoidance maneuver and inattention or fatigue. Roadway and roadside geometric design features (e.g., lane widths and clear zones) play a significant role in whether human error results in a crash. In this paper, we present a hazard-based duration model to investigate the distance traveled by an errant vehicle in a run-off-road crash, the stopping hazard rates, and associated risk factors. For this study, we obtained five years’ (2010–2014) of crash data related to roadway departures (i.e., overturn and fixed-object crashes) from the Federal Highway Administration's Highway Safety Information System Database. The results indicate that over 50% of the observed vehicles traveled no more than 36 ft. in a ROR crash and 25% of the observed vehicles traveled at least 78 ft. We also found that seasonal, roadway, and crash variables, along with vehicle information and driver characteristics significantly contributed to the distances traveled by errant vehicles in ROR crashes. This paper presents methodological empirical evidence that the Cox proportional-hazards model is appropriate for investigating the distances traveled by errant vehicles in ROR crashes. In addition, it also provides valuable information for traffic design and management agencies to improve roadside design policies and implementing appropriately forgiving roadsides for errant vehicles.

Original languageEnglish (US)
Pages (from-to)165-173
Number of pages9
JournalAccident Analysis and Prevention
Volume120
DOIs
StatePublished - Nov 2018

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Safety, Risk, Reliability and Quality
  • Public Health, Environmental and Occupational Health

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