Real-time crash risk prediction is expected to play a crucial role in preventing traffic accidents. However, most existing studies only focus on freeways rather than urban arterials. This paper proposes a real-time crash risk prediction model on arterials using a long short-term memory convolutional neural network (LSTM-CNN). This model can explicitly learn from the various features, such as traffic flow characteristics, signal timing, and weather conditions. Specifically, LSTM captures the long-term dependency while CNN extracts the time-invariant features. The synthetic minority over-sampling technique (SMOTE) is used for resampling the training dataset. Five common models are developed to compare the results with the proposed model, such as the XGBoost, Bayesian Logistics Regression, LSTM, etc. Experiments suggest that the proposed model outperforms others in terms of Area Under the Curve (AUC) value, sensitivity, and false alarm rate. The findings of this paper indicate the promising performance of using LSTM-CNN to predict real-time crash risk on arterials.
Keywords: Deep learning; Real-time crash risk; Recurrent neural network; Urban arterials.
Published by Elsevier Ltd.