Expressway Crash Prediction based on Traffic Big Data

With the development of society, the number of vehicles increases rapidly. The vehicle plays an important role in people's life, however the problem of traffic safety caused by vehicles has also become increasingly prominent. In China, the high crash rate and casualty rate on expressways have always troubled traffic management department. So crash prediction on expressway becomes vital. Conventionally, crash prediction is based on traffic flow data. These data do not contain all the necessary factors. In this paper, we propose a method of prediction using real-world data, including historical accident data, road geometry data, vehicle speed data, and weather data. We treat the crash prediction problem as a binary classification problem. For classification, sample imbalanced is a great challenge in practice. Modifying sample weights is applied to handle this challenge. Three machine learning classification techniques, namely Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and Xgboost, are considered to carry out the crash prediction task respectively. The best recall and precision rate of these models are respectively 0.764253 and 0.01062. The proposed method can be integrated into urban traffic control systems toward police dispatch and crash prevention.