Developing a rear-end crash risk algorithm under fog conditions using real-time data

Abstract

This study intends to evaluate the rear-end collision risk under fog conditions considering reduced visibility by introducing a new algorithm. Based on the proposed algorithm, the minimum stopping distance of the leading and following vehicles can be calculated with traffic and weather data and compared. 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 minimum stopping distances are suggested correspondingly. The visibility distance is collected by a visibility detection system and the clearance distance is obtained from a traffic detector which can provide individual vehicle data. By comparing the minimum stopping distances of the following and leading vehicles, the potential rear-end collision can be identified. Subsequently, statistical tests are conducted to analyze the rear-end collision risk under different situations. Furthermore, logistic and negative binomial models are estimated by using individual and aggregated 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 found 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 algorithm 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.