Calibrating driving behavior with microscopic measurement data

In this paper we introduce the use of microscopic measurement data to calibrate driving behavior in microscopic simulation models. Based on data gathered from remote sensing and equipped vehicle data, a dynamic decision network has been designed to determine drivers´ action to certain situations. Since this kind of pattern matching leads to a deterministic driving behavior, which cannot be assumed for reality, a noise is added to the perception of the individual driver which reflects human behavior in misjudging distances and speeds during the driving task