Towards Safety-Risk Prediction of CBTC Systems with Deep Learning and Formal Methods

Abstract

Communication-Based Train Control System (CBTC) system is an automated system for train control based on bidirectional train-ground communication. Safety-risk estimation is a vital approach that strives to guide the CBTC system to guarantee the safe operation of vehicles. We propose a deep learning method to predict safety-risk states that combined with formal methods. First, the impact factors are selected, and the movement authorization (MA) failure rate is calculated by statistical model checking. Then, we use a deep neural network to model the relationship between the safe-risk states and the train operation status. Experimental results show that our method can achieve an accuracy of 97.4% on safety-risk prediction, and exceeds the baseline methods.