Abstract
Traffic routing is a central challenge in the context of urban areas, with a direct impact on personal mobility, traffic congestion, and air pollution. In the last decade, the possibilities for traffic flow control have improved together with the corresponding management systems. However, the lack of real-time traffic flow information with a city-wide coverage is a major limiting factor for an optimum operation. Smart City concepts seek to tackle these challenges in the future by combining sensing, communications, distributed information, and actuation. This paper presents an integrated approach that combines smart street lamps with traffic sensing technology. More specifically, infrastructure-based ultrasonic sensors, which are deployed together with a street light system, are used for multilane traffic participant detection and classification. Application of these sensors in time-varying reflective environments posed an unresolved problem for many ultrasonic sensing solutions in the past and therefore widely limited the dissemination of this technology. We present a solution using an algorithmic approach that combines statistical standardization with clustering techniques from the field of unsupervised learning. By using a multilevel communication concept, centralized and decentralized traffic information fusion is possible. The evaluation is based on results from automotive test track measurements and several European real-world installations.
Highlights
In the context of the Internet of Things (IoT), the integral transformation process of urban infrastructure into intelligent and connected devices plays an important role for future mobility
It was shown that sidefire ultrasonic sensing is a viable option for multilane traffic participant sensing, giving very good performance results in real-world urban scenarios with a single sensor
The functionality is enabled by a novel combination of standardization techniques based on windowed statistics and a modified density-based clustering algorithm, together called Density-Based Statistical Clustering (DBStaC)
Summary
In the context of the Internet of Things (IoT), the integral transformation process of urban infrastructure into intelligent and connected devices plays an important role for future mobility. The monitoring of multiple lanes is possible with our setup, while, at the same time, the requirements in terms of algorithmic evaluation and processing are increased in comparison to these previously existing systems With these improvements, our approach allows operation in a highly reflective acoustic environment, which is present in urban areas and permits mounting the sensor on the side of the street. This is enabled by a new approach with a combination of statistical signal processing, clustering, and inference algorithms for traffic participant object detection This sidefire ultrasonic traffic sensing technique is part of a development which focuses on intelligent infrastructure solutions, on intelligent street lights.
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