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<p><strong>Abstract.</strong> The environment of the vehicle can significantly influence the driving situation. Which conditions lead to unsafe driving behaviour is not always clear, also not to a human driver, as the causes might be unconscious, and thus cannot be revealed by expert interviews. Therefore, it is important to investigate how such situations can be reliably detected, and then search for their triggers. It is conceivable that such insecure situations (e.g. near-accidents, U-turns, avoiding obstacles) are reflected, for example, as anomalies in the movement trajectories of road users.</p><p>Collecting real world traffic data in driving studies is very time consuming and expensive. However, a lot of roads or public areas are already monitored with video cameras. In addition, nowadays more and more of such video data is made publicly available over the internet so that the amount of free video data is increasing. This research will exploit the use of such kind of opportunistic VGI. In the paper the first step of an automatic analysis are presented, namely: to introduce a real time processing pipeline to extract road user trajectories from surveillance video data.</p>

Abstract. The wide usage of GPS-equipped devices enables the mass recording of vehicle movement trajectories describing the movement behavior of the traffic participants. An important aspect of the road traffic is the impact of anomalies, like accidents, on traffic flow. Accidents are especially important as they contribute to the the aspects of safety and also influence travel time estimations. In this paper, the impact of accidents is determined based on a massive GPS trajectory and accident dataset. Due to the missing precise date of the accidents in the data set used, first, the date of the accident is estimated based on the speed profile at the accident time. Further, the temporal impact of the accident is estimated using the speed profile of the whole day. The approach is applied in an experiment on a one month subset of the datasets. The results show that more than 72% of the accident dates are identified and the impact on the temporal dimension is approximated. Moreover, it can be seen that accidents during the rush hours and on high frequency road types (e.g. motorways, trunks or primaries) have an increasing effect on the impact duration on the traffic flow.