Traffic routing is a central challenge in the context of urban areas, with a direct impact on personal mobility, traffic congestion,\nand air pollution. In the last decade, the possibilities for traffic flow control have improved together with the corresponding\nmanagement systems.However, the lack of real-time traffic flow information with a city-wide coverage is amajor limiting factor for\nan optimum operation. Smart City concepts seek to tackle these challenges in the future by combining sensing, communications,\ndistributed information, and actuation. This paper presents an integrated approach that combines smart street lamps with traffic\nsensing technology. More specifically, infrastructure-based ultrasonic sensors, which are deployed together with a street light\nsystem, are used formultilane traffic participant detection and classification. Application of these sensors in time-varying reflective\nenvironments posed an unresolved problem for many ultrasonic sensing solutions in the past and therefore widely limited the\ndissemination of this technology. We present a solution using an algorithmic approach that combines statistical standardization\nwith clustering techniques from the field of unsupervised learning. By using a multilevel communication concept, centralized and\ndecentralized traffic information fusion is possible. The evaluation is based on results from automotive test track measurements\nand several European real-world installations.
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