Automated vehicles are expected to play a major role in road traffic within the next decades. Thus, it becomes necessary to manage the oncoming partly automated traffic between classical and automated vehicles. In this context, human behavior represents a major source of uncertainty. In order to make different driving behavior as predictable as possible, we chose a statistical approach by collecting empirical data from classical road traffic. For this purpose, a stationary infrared sensor system including multiple measuring units to detect passing vehicles was developed. The involved sensors were attached to lamp posts next to an urban road with a speed limit of 50 km/h. From the generated data set, a statistical analysis of the change in temporal headway between consecutive vehicles is derived. Additionally, an empirically ascertained vehicle speed distribution is presented. Last but not least, a suitable heavy tail distribution is used to fit the underlying data of the occuring temporal headways. All in all, the presented results could help an automated vehicle to merge into the flowing traffic on a major road in an efficient way considering safety, energy, and comfort criteria.