ITS can play a significant role in the improvement of traffic flow, traffic safety and greenhouse gas emissions. However, the implementation of Advanced Driver Assistance Systems may lead to adaptation effects in longitudinal driving behavior following driver distraction. It was however not yet clear how to model these adaptation effects in driving behavior mathematically and on which theoretical framework this should be grounded. To this end in this contribution we introduce a theoretical framework based on the Task-Capability-Interface model by Fuller and integrate this model into the Intelligent Driver Model. Through a case study using simulations we show that this integration provides a relatively adequate description of the effects of driver distraction. The contribution finishes with conclusions and recommendations for future research.

Cooperative traffic management may have beneficial effects on society. However, the efficiency of the measures are largely dependent on behavior of the road users. The application of these measures may be assumed to have an influence on complexity of the driving conditions, with in turn an influence on behavior. Mathematical models of driving behavior incorporated in microscopic simulation software packages are currently inadequate to capture this influence. In order to adequately incorporate this influence an empirically underpinned quantification of the complexity of the driving conditions is needed. In this contribution we take some first steps towards the development of a quantification of traffic system complexity using fuzzy entropy.We present the proposed method and show the workings of the method using a case study. The contribution finishes with a discussion section and recommendations for future research.

Advanced Driver Assistance Systems may have a positive effect on traffic flow efficiency, the environment, safety and comfort. However these systems may have a negative impact on driving behavior following a change in driver workload. It is therefore crucial to develop a so-called driver workload manager. In order to manage driver workload an adequate classification of driver workload is indispensible. In this contribution we propose to classify and predict driver workload through physiological indicators of driver workload, driver characteristics and characteristics of the driving condition using a neural network modeling approach. We show that the proposed network yields a very good classification of driver workload. The contribution finishes with a discussion section and recommendations for future research.