Automatic incident detection on freeways based on Bluetooth traffic monitoring

Abstract

A novel automatic incident detection (AID) method for freeways, based on the use of data provided by Bluetooth sensors and an unsupervised anomaly detection approach, is presented. The two main advantages of the proposed AID system are: (i) the use of Bluetooth sensors offers several practical advantages over inductive loop detectors (ILD), which is one of the preferred sensing technology for traffic flow; and (ii) the unsupervised anomaly detection approach builds a model without the need of incident information. A common problem when designing an AID system is that incident information, i.e., ground-truth data, with enough accuracy is seldom available. Isolation forest is the unsupervised anomaly detection approach adopted in this work. This method is based on characterizing anomalous traffic conditions by exploiting the fact that anomalies tend to be isolated. The most remarkable feature of this anomaly detection method is its high detection performance while having a very simple tuning procedure and an extremely low computational demand. Finally, the effectiveness of the presented AID method is demonstrated using real traffic data collected by a network of Bluetooth sensors installed in Ayalon Highway, Tel Aviv.