Developing an algorithm to assess the rear-end collision risk under fog conditions using real-time data

This study aims to propose a new algorithm to evaluate the rear-end collision risk under fog conditions considering reduced visibility. The proposed algorithm compares the safe stopping distance of the leading and following vehicles. According to the relationship between clearance distance between the two consecutive vehicles and visibility distance, the car-following maneuver is divided into different situations and the algorithms to calculate the safe stopping distances are suggested correspondingly. The visibility distance is collected by a new visibility detection system with adaptive learning modules and the clearance distance is obtained from a vehicle-based detector. By comparing the safe stopping distances of the following and leading vehicles, the potential rear-end collision can be identified. Subsequently, statistical tests are conducted to analyze rear-end collision risk and compare the different impact of reduced visibility on the collision risk for different vehicle types and lanes. Furthermore, random parameters logistic and negative binomial models are estimated by using individual vehicle data and aggregated traffic flow data, respectively, in order to explore the relationship between the potential rear-end crash and the reduced visibility together with other traffic parameters. The results suggest that the proposed algorithm works well in evaluating rear-end collision risk under fog conditions. It is found that reduced visibility has significant impact on the rear-end collision risk and the impact vary by the different vehicle types and by lane. Further, it is concluded that the driving maneuver of the leading and following vehicles can affect the rear-end collision risk. It is expected that the proposed algorithm can be implemented in a Traffic Management context to improve road safety under fog conditions. Specifically, it is suggested to implement the proposed algorithms in real-time and integrate it with ITS technologies such as Variable Speed Limit (VSL) and Dynamic Message Signs (DMS) to enhance traffic safety when the visibility declines. This car following algorithm could also be extended to adapt for the advent of Connected Vehicles in Fog conditions.The authors acknowledge the financial support of the Florida Department of Transportation. All opinions expressed in this study are those of the authors and not the sponsor