A Risk-Based Systematic Method for Identifying Fog-Related Crash Prone Locations

Abstract

Fog is one of the most influential factors in fatal crashes because of reduced visibility. This study aims to propose a systematic safety analysis framework for selecting fog-crash-prone areas on freeways. To achieve these goals, the spatial analysis in ArcGIS was combined with the latent class cluster-based crash severity estimation models. Nine latent class cluster-based crash severity estimation models were built. Fog events led to a statistically significant increase in the likelihood of fatal crashes in two of the nine models. Comparing the ArcGIS spatial clusters of fog-related exposure with the fatal crash-prone freeway segments, 28 freeway segments were found to be fog-crash-prone areas where safety improvements are required, particularly in foggy weather. Based on the spatial patterns of the fog-crash-prone freeway segments, this study concludes that the current standard for fog-crash-prone area selection should be modified to apply spatially different standards over the Korean freeway network. This study is the first data-driven study to comprehensively examine the effects of fog visibility levels and frequencies on fatal crashes in the entire Korean freeway system. The findings provide meaningful insights to the policy decision making for fog-related policy changes, highway safety enhancement and active traffic management strategies.