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Although there has been substantial research in systems analytic 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 urban rail transit safety and efficiency. 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-totrain 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 if for unbalance dataset. This model consistently outperforms the deep belief network trained in Accuracy, Precision, Recall and F1-score for hazard-risk prediction problem.
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Jing Liu is a professor in the Computer Science and Software Engineering Institute of East China Normal University, Shanghai. Her research work focuses on Software Modeling, Analysis and Verification, High Performance Computing. She won the First Place Natural Science Award by Ministry of education via ¡°Model based trustworthy software theories and development methods¡± in 2012. She is a leader of a project of National Natural Science Foundation of China. In the domestic and foreign academic journals and important international academic conference she published more than 80 papers. Previously, she was a Principle Investigator of several projects, including Model Driven Development Technology of High Dependable Software (863 program), A Strategy for Model Construction and Integration (National Scientific Foundation of China) etc.
