Comparative study on the effectiveness of various types of road traffic intensity detectors

Vehicle detection and speed measurements are crucial tasks in traffic monitoring systems. In this work, we focus on several types of electronic sensors, operating on different physical principles in order to compare their effectiveness in real traffic conditions. Commercial solutions are based on road tubes, microwave sensors, LiDARs, and video cameras. Distributed traffic monitoring systems require a high number of monitoring stations. In order to improve the accuracy of traffic monitoring, several modalities, complementing each other, may be used in the monitoring stations. In this paper, we propose a multimodal approach to traffic monitoring, using sensors and signal processing algorithms developed specifically for the described task. The aim of the work described here is to test each modality in a real-life scenario, assess their accuracy and to evaluate their usefulness for multimodal traffic monitoring stations. The modalities described in the paper are: Doppler sensor with custom signal processing, video analysis based on cameras and neural networks (employing deep learning algorithms), audio monitoring based on an acoustic vector sensor developed by the authors, as well as LiDAR and Bluetooth as supplementary means of traffic monitoring. Additionally, road tubes and a commercial video-based monitoring system were used in order to provide reference data. Consequently, we can present in this paper a comparative study on the effectiveness of traffic sensors operating based on different principles of work.