Microscopic traffic simulation is an invaluable tool for traffic research. In recent years, both the scope of research and the capabilities of the tools have been extended considerably. This article presents the latest developments concerning intermodal traffic solutions, simulator coupling and model development and validation on the example of the open source traffic simulator SUMO.
For emergency vehicle drivers it is an important task to reach the incident location as fast as possible. Therefore a self-organizing green wave could help emergency vehicles to accomplish this goal. This study presents an approach how emergency vehicle can be prioritized at traffic lights and simulates the possible benefit for the emergency vehicle. Traffic data from vehicular communication can be used to find the optimal timing for the traffic light to modify the existing traffic phases and reduce the possible negative impact on other traffic participants.
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.
To understand the influence of the automated shuttles on active modes as pedestrians and bicyclists, data was collected at the pilot site Linköping within the context of the European project SHOW, where AS provide regular transport service on the campus and run along a corridor restricted to bike and pedestrian traffic with pre-defined stops. Three types of data were collected, i.e. video data, shuttle data and traffic counts with use of Telraam, while the first one was the main data source for analyzing VRU behaviors and the others were used for checking the validity of video data. The investigation mainly focused on VRU’s space usage, speed, acceleration and lateral position and distance with and without AS presence. Bikes maneuvers, compatible with overtaking, were also examined. The analysis results can help for simulation model improvement.
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