Intelligent Hazard-Risk Prediction Model for Train Control Systems

• Published in: IEEE transactions on intelligent transportation systems Vol. 21; no. 11; pp. 4693 - 4704
• Main Authors: Liu, Jing, Zhang, Yan, Han, Jiazhen, He, Jifeng, Sun, Junfeng, Zhou, Tingliang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Belief networks; Communication; communication-based train control system; Communications systems; Control systems; deep learning; Hazard assessment; Hazards; long-short-term memory (LSTM); Neural networks; Operational hazards; Prediction models; Predictions; Predictive models; Rails; Recall; Recurrent neural networks; Risk analysis; Risk assessment; Risk communication; Risk prediction; Safety; statistical model checking; Statistical models; Systems architecture; Urban rail
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2019.2945333

Although there has been substantial research in system analytics for risk assessment in traditional methods, little work has been done for safety risk prediction in communication-based train control (CBTC) system, especially intelligently predicting risk caused by the uncertainty in the system operation. Risk prediction and assessment of hazards in train control systems are vital for the safety and efficiency of urban rail transit. In this paper, we propose an intelligent hazard-risk prediction model based on a deep recurrent neural network for a new communication-mode CBTC system. First, a train-to-train communication-based train control (T2T-CBTC) system is proposed to improve the drawback of CBTC in information-exchanging mode. Then we design a risk prediction feature selection and generation method and estimate a critical function feature in the T2T-CBTC system by statistical model checking. Finally, we construct our intelligent hazard-risk prediction model based on a deep recurrent neural network using a long-short-term memory (LSTM) network. The model had excellent risk prediction classification results and performance in our experiment, even for unbalanced data set. This model consistently outperforms the deep belief network trained in Accuracy, Precision, Recall and F1-score for the hazard-risk prediction problem. Specifically, the mean accuracy is 97.2% and mean F1-score is 93.9% in overall performance of model. The improvements of our model against DBN model are 8.2% for Precision, 7% for Recall and 8% for F1-score.