WiTraffic: Low-cost and non-intrusive traffic monitoring system using WiFi

The traffic monitoring system is an imperative tool for traffic analysis and transportation planning. In this paper, we present WiTraffic: the first WiFi-based traffic monitoring system. Compared with existing solutions, it is non-intrusive, cost- effective, and easy-to-deploy. Unique WiFi Channel State Information (CSI) patterns of passing vehicles are captured and analyzed to effectively perform vehicle classification, lane detection, and speed estimation. A machine learning technique is adopted to train vehicle classification models and efficiently categorize vehicles. An Earth Mover's Distance (EMD)-based vehicle lane detection algorithm and vehicle speed estimation mechanism are proposed to further utilize WiFi CSI to identify the lane in which a vehicle is located and to estimate the vehicle speed. We implemented WiTraffic with off-the-shelf WiFi devices and performed real-world experiments with over a week of field data collection in both local roads and highways. The results show that the mean classification accuracy, lane detection accuracy for both local road and highway settings are around 96%, and 95%, respectively. The average root-mean- square error (RMSE) of the proposed CSI-based speed estimation method on a highway was 5mph in our experimental settings.