Over the past years, the bicycle has gained importance as a means of transportation in big cities. The use and acceptance of a bicycle as being an evolving means of transportation is highly linked to its transportation safety. Still, the risk of accidents is a dominant barrier. Even though the Federal Ministry of Transport, Building and Urban Development established a National Cycling Plan to enhance cycling and improve safety aspects, serious accidents still occur. Even if the number of traffic accidents is declining in Berlin, the consequences of bicycle accidents with physical injury are characterised by increasing results. Thus, it is proved that more than half of the accidents that involve bicyclists are caused by the cyclist itself. To understand causes of accidents and to eventually arrange preventive measures and enhance cyclists' safety, critical situations were detected. The application is based on cyclists' trajectories generated from video sequences. As a result, atypical and dangerous traffic situations can be identified automatically whereas rule violations can be detected manually. First experiences at an intersection in Berlin show a general applicability of this approach, which has to be widely tested at other intersections.

In this paper an early vision tracking algorithm particularly adapted to the tracking of road users in video image sequences is presented. The algorithm is an enhanced version of the regression based motion estimator in Lucas-Kanade style. Robust regression algorithms work in the presence of outliers, while one distinct property of the proposed algorithm is that it can handle with datasets including 90% outliers. Robust regression involves finding the global minimum of a cost function, where the cost function measures if the motion model is conform with the measured data. The minimization task can be addressed with the graduated non convexity (GNC) heuristics. GNC is a scale space analysis of the cost function in parameter space. Although the approach is elegant and reasonable, several attempts to use GNC for solving robust regression tasks known from literature failed in the past. The main improvement of the proposed method compared with prior approaches is the use of a preconditioning technique to avoid GNC from getting stuck in a local minimum.

Air pollution has been a serious problem in China for many years. One of the main reasons for this is the rapid growth in motorized traffic which results in lots of traffic congestion in metropolitan areas. This study aims to monitor and reduce traffic's impact on air quality at intersections, with the use of the object detecting and tracking technique and traffic signal control optimization. A field campaign is executed at a selected intersection in Hefei, China and the collected emission data is used for examining and ensuring how reasonable the simulated emission productions are. The simulative approach shows that the proposed signal control method greatly increases the efficiency of the traffic operation and, at the same time, reduces the amount of emission production. A field test of the proposed method is under preparation for verifying the simulation results and will be carried out within two months.

The detection of atypical trajectories and events in road traffic is a challenging task for the implementation of an intelligent transportation system. It also provides information for optimizing the traffic flow and mitigating risks of accidents without the need to observe individual traffic participants. For detecting such events two methods representing the state of the art are compared: a map-based trajectory analyzer and a neural network, the Self Organizing Map-both applicable with unsupervised learning. The two compared algorithms detect atypical trajectories by modeling the probability function of trajectory features representing the object state at every trajectory point containing location, speed and acceleration values. The map-based approach was extended and improved by pre-clustering the trajectories with regard to their relation (e.g. vehicle turning left/going straight ahead). The Self Organizing Map algorithm uses vector quantization and prototyping of feature vectors and, thus, does not need any preliminary work. Both methods are evaluated by experiments using the same data which allows strengths and weaknesses to be revealed. The data base for evaluation consists of trajectories from traffic surveillance cameras at an intersection and simulated trajectories.

The automated detection of atypical and critical traffic situations is essentially important to help to understand driver behaviour, to find functional correlations between traffic conflicts and real accidents, and eventually, to prevent, particularly severe accidents. In this paper, a tool chain is introduced that enables fully automated traffic situation detection in wide-area traffic on the basis of a single camera. The tool chain takes into account novel powerful methods for object detection, classification and tracking on the basis of robust regression with preconditioning. Moreover, the tool chain considers methods for traffic situation detection and classification on the basis of probabilistic approaches and eventually, traffic event recording. The approach was tested at an ungated level crossing in the small town Bienrode, which is a district of Brunswick, Germany. It is shown that atypical situations, e.g. overtaking, braking, stopping, inadequate speeds, and accelerations, as well as critical situations, e.g. tailgating, can be detected within a range of up to 120 m distance of the camera automatically. The approach enables new ways of analysing traffic areas with regard to traffic safety and performance. The results shown in this paper were obtained in the project OptiSiLK, whose abbreviation means "Optimisation of the safety and the performance at intersections of different traffic modes". OptiSiLK was funded by the Ministry for Science and Culture of the State of Lower Saxony (MWK).