In this paper we perform the analysis of the popular TPMS (tire pressure monitoring systems) and their application for traffic management purposes. In particular, we evaluate several of the commercially available TPMS devices and analyze their architecture and communication features. Furthermore, we propose the architecture of an external sensor device used for effective eavesdropping of TPMS ID data. Finally, we evaluate the possibility of utilizing such systems for the identification and re-identification of traffic participants using the unique ID of TPMS sensors.

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This paper presents how emergency vehicles can be modeled and simulated in the microscopic traffic simulation SUMO (Simulation of Urban MObility). The special rights of emergency vehicles are implemented in the SUMO framework and can be switched off and on in the simulation with a blue light device. The surrounding traffic reacts accordingly to the emergency vehicle and form an emergency lane. In addition real world data from emergency vehicles are used to evaluate the driving behavior of emergency vehicles and compare it to real world and simulated vehicle characteristics. The evaluation results show that the simulated vehicles pass an intersection generally faster than in real world. For emergency vehicle a time saving of in average one second at a single intersection could be measured for right turning vehicles.

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.

In this paper we present initial results in utilization of TPMS (Tire Pressure Monitoring System) for collecting traffic data and deriving traffic information, i.e. travel times. The obtained results show that current detection ratio is less than 5 % and the obtained travel times are in consistency with referent data. The experiment is performed on DLR test track in Berlin. In particular, architecture of TPMS receiver is proposed. Next, the algorithm for reducing data redundancy and for deriving traffic information is introduced. Finally the obtained results are presented.