Investigating the uniqueness of crash injury severity in freeway tunnels: A comparative study in Guizhou, China

J Safety Res. 2021 Jun:77:105-113. doi: 10.1016/j.jsr.2021.02.008. Epub 2021 Mar 26.

Abstract

Introduction: With the rapid development of transportation infrastructures in precipitous areas, the mileage of freeway tunnels in China has been mounting during the past decade. Provided the semi-constrained space and the monotonous driving environment of freeway tunnels, safety concerns still remain. This study aims to investigate the uniqueness of the relationships between crash severity in freeway tunnels and various contributory factors.

Method: The information of 10,081 crashes in the entire freeway network of Guizhou Province, China in 2018 is adopted, from which a subset of 591 crashes in tunnels is extracted. To address spatial variations across various road segments, a two-level binary logistic approach is applied to model crash severity in freeway tunnels. A similar model is also established for crash severity on general freeways as a benchmark.

Results: The uniqueness of crash severity in tunnels mainly includes three aspects: (a) the road-segment-level effects are quantifiable with the environmental factors for crash severity in tunnels, but only exist in the random effects for general freeways; (b) tunnel has a significantly higher propensity to cause severe injury in a crash than other locations of a freeway; and (c) different influential factors and levels of contributions are found to crash severity in tunnels compared with on general freeways. Factors including speed limit, tunnel length, truck involvement, rear-end crash, rainy and foggy weather and sequential crash have positive contributions to crash severity in freeway tunnels. Practical applications: Policy implications for traffic control and management are advised to improve traffic safety level in freeway tunnels.

Keywords: Crash injury severity; Freeway tunnel; Multilevel model; Road safety; Unobserved heterogeneity.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidents, Traffic / classification
  • Accidents, Traffic / statistics & numerical data*
  • Automobile Driving / statistics & numerical data*
  • China
  • Humans