Samenvatting

This research is an explorative study to look for the potential to predict traffic density from driver behavior using signals collected from the Controller Area Network (CAN) bus. The hypothesis is that driver behavior is influenced by traffic density in such a way that an approximation of the traffic density can be determined from changes in the driver behavior. Machine learning will be employed to correlate a selection of commonly available sensors on cars to the traffic density. Challenges in the processing of the data for this purpose will be outlined. This study is restricted to straight roads in order to isolate the steering behavior attributable to the traffic state influences rather than following the curve in the road. The results are encouraging that the correlation between driver behavior and traffic density can be established. An overall accuracy of over 95% is achieved with a precision of 92%. The recall rate however is low most likely caused by over-fitting due to the unbalanced dataset.