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Traffic within cities has increased in the last decades due to increasing mobility, changing mobility behavior and new mobility offerings. These accelerating changes make it increasingly difficult for responsible authorities or other stakeholders to predict mobility behavior, to configure traffic rules or to size roads, bridges and parking lots. Traffic simulations are a powerful tool for estimating and evaluating current and future mobility, upcoming traffic services and automated functionalities in the domain of traffic management. For being able to simulate a complex real-world traffic environment and traffic incidents, the simulation environment needs to fulfill requirements from real-world scenarios related to sensor-based data processing. In addition, it must be possible to include latest advancements of technology in the simulation environment, for instance, (1) connected intersections that communicate with each other, (2) a complex and flexible set of rules for traffic sign control and traffic management or a well-defined data processing of relevant sensor data. In this paper we therefore define requirements for a traffic simulation based on a complex real-world scenario in Germany. The project addresses major urban challenges and aims at demonstrating the contribution that the upcoming 5G mobile generation can make to solving real-time traffic flow optimization. In a second step, we investigate in detail if the simulation environment SUMO (Simulation of Urban Mobility) fulfills the postulated requirements. Thirdly, we propose a technical concept to close the gap of the uncovered requirements for later implementation.
In the past years the progress in the development of autonomous vehicles has increased tremendously. There are still technical, infrastructural and regulative obstacles which need to be overcome. However, there is a clear consent among experts that fully autonomous vehicles (level 5 of driving automation) will become reality in the coming years or at least in the coming decades. When fully autonomous vehicles are widely available for a fair trip price and when they can easily be utilized, a big shift from privately owned cars to carsharing will happen. On the one hand, this shift can bring a lot of chances for cities like the need of less parking space. But on the other hand, there is the risk of an increased traffic when walking or biking trips are substituted by trips with shared autonomous vehicle fleets. While the expected social, ecological and economical impact of widely used shared autonomous vehicle fleets is tremendous, there are hardly any scientific studies or data available for the effects on cities and municipalities. The research project KI4ROBOFLEET addressed this demand. A result of the project was SUMO4AV, a simulation environment for shared autonomous vehicle fleets, which we present in this paper. This simulation tool is based on SUMO, an open-source traffic simulation package. SUMO4AV can support city planners and carsharing companies to evaluate the chances and risks of running shared autonomous fleets in their local environment with their specific infrastructure. At its core it comprises the mapping of OpenStreetMap1 entities into SUMO objects as well as a Scenario Builder to create different operation scenarios for autonomous driving. Additionally, the simulation tool offers a recursive execution with different fleet sizes and optimization strategies evaluated by economic and ecologic parameters. As evaluation of the toolset a simulation of an ordinary scenario was performed. The workflow to simulate the scenario for shared autonomous vehicle fleets was successfully processed with the SUMO4AV environment.
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