Towards a Stochastic Model of Driving Behavior under Adverse Conditions

Adverse conditions have been shown to have a substantial impact on traffic flow operations following substantial adaptation effects in driving behavior. In order to determine whether driver support systems are effective under these circumstances, it is crucial to capture stochasticity due to human factors in mathematical models of driving behavior. To this end in this contribution we introduce a new stochastic car-following model based on psycho-spacing theory using a Bayesian network modeling approach with parameter learning. We show that this model yields a relative adequate prediction of longitudinal driving behavior in case of adverse conditions through several examples. The paper is concluded with a discussion section and recommendations for future research.