Longitudinal driving behavior under adverse weather conditions: adaptation effects, model performance and freeway capacity in case of fog

摘要

Adverse weather conditions have been shown to have a substantial impact on traffic flow operations. It is however unclear which adaptation effects in actual longitudinal driving behavior underlie this impact, how these adaptation effects relate to freeway capacity as well as to what extent current mathematical models of car-following behavior are adequate in incorporating these adaptation effects. In this regard a driving simulator experiment with a repeated measures design was performed in order to examine the influence of fog on adaptation effects, freeway capacity and parameter value changes and model performance of the Helly model and Intelligent Driver Model. From the results followed that fog led to a decrease in speed as well as in acceleration. Furthermore a substantial increase in distance to the lead vehicle was observed. These effects were implemented and simulated in a traffic simulation model. A substantial reduction in freeway capacity was found. This stresses the need to possess models of driving behavior, which are adequate in describing and predicting these adaptation effects. From the estimation results of the Helly model and IDM using a calibration approach for joint estimation followed that sensitivity factors, maximum acceleration and deceleration decreased substantially after the start of the adverse weather condition. Parameters representing headway increased significantly. Furthermore it followed from the results that the estimated models decreased in performance after the start of the adverse weather conditions