Adverse conditions (emergency situations, adverse weather conditions, freeway incidents) have been shown to have a substantial impact on traffic flow operations. It is however unclear to what extent the conditions impact longitudinal driving behaviour and what the determinants of these changes in driving behaviour are. Furthermore, it is not yet clear how these changes in driving behaviour can best be modelled. To this end the author performed three extensive driving simulator experiments intended to investigate the influence of emergency situations, adverse weather conditions and freeway incidents on empirical longitudinal driving behaviour as well as driver workload. Furthermore he determined the influence of these conditions on parameter values and model performance of an often used car-following model, i.e., the Intelligent Driver Model. The author also determined changes in the position of so-called action points in a psycho-spacing model and took some first steps towards the development of a new stochastic car following model based on a Bayesian network modelling approach. (Author/publisher)
Abstract