Freeway accident detection and classification based on the multi-vehicle trajectory data and deep learning model

The freeway accident detection and classification have attracted much attention of researchers in the past decades. With the popularity of Global Navigation Satellite System (GNSS) on mobile phones and onboard equipment, increasing amounts of real-time vehicle trajectory data can be obtained more and more easily, which provides a potential way to use the multi-vehicle trajectory data to detect and classify an accident on freeways. The data has the advantages of low cost, high penetration, high real-time performance, and being robust to the outdoor environment. Therefore, this paper proposes a new method for accident detection and classification based on the multi-vehicle trajectory data. Different from the existing methods using GNSS positioning data, the proposed method not only uses the position information of the related vehicles but also tries to capture the development tendencies of the trajectories of accident vehicles over a period of time. A Deep Convolutional Neural Network model is developed to recognize an accident from the normal driving of vehicles and also identify the type of the accident, and the six types of traffic accidents are considered in this study. To train and test the proposed model, the simulated trajectory data is generated based on PC-Crash, including the normal driving trajectories and the trajectories before, in, and after an accident. The results indicate that the detection accuracy of the proposed method can reach up to 100%, and the classification accuracy can reach up to 95%, which both outperform the existing methods using other data. In addition, to ensure the robustness of the detection accuracy, at least 1 s of duration and 5 Hz of frequency for the trajectory data should be adopted in practice. The study will help to accurately and fastly detect an accident, recognize the accident type, and future judge who is liable for the accident.