Real-time assessment of fog-related crashes using airport weather data: A feasibility analysis

• Utilizing weather data collected from adjacent airports in real-time safety assessment.
• Kernel density estimation (KDE) in ArcGIS was used to identify the hotspots.
• Bayesian logistic regression was utilized to analyze 6-year (2005–2010) crash data.
• Airports’ weather data are good predictors to assess increased risk on highways.

The effect of reduction of visibility on crash occurrence has recently been a major concern. Although visibility detection systems can help to mitigate the increased hazard of limited-visibility, such systems are not widely implemented and many locations with no systems are experiencing considerable number of fatal crashes due to reduction in visibility caused by fog and inclement weather. On the other hand, airports’ weather stations continuously monitor all climate parameters in real-time, and the gathered data may be utilized to mitigate the increased risk for the adjacent roadways. This study aims to examine the viability of using airport weather information in real-time road crash risk assessment in locations with recurrent fog problems. Bayesian logistic regression was utilized to link six years (2005–2010) of historical crash data to real-time weather information collected from eight airports in the State of Florida, roadway characteristics and aggregate traffic parameters. The results from this research indicate that real-time weather data collected from adjacent airports are good predictors to assess increased risk on highways.