Research Article
Intelligent-Prediction Model of Safety-Risk for CBTC System by Deep Neural Network
@INPROCEEDINGS{10.1007/978-3-030-30146-0_45, author={Yan Zhang and Jing Liu and Junfeng Sun and Xiang Chen and Tingliang Zhou}, title={Intelligent-Prediction Model of Safety-Risk for CBTC System by Deep Neural Network}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 15th EAI International Conference, CollaborateCom 2019, London, UK, August 19-22, 2019, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={8}, keywords={Risk estimation Deep learning Communication-based train control system Statistic model checking}, doi={10.1007/978-3-030-30146-0_45} }- Yan Zhang
Jing Liu
Junfeng Sun
Xiang Chen
Tingliang Zhou
Year: 2019
Intelligent-Prediction Model of Safety-Risk for CBTC System by Deep Neural Network
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-30146-0_45
Abstract
Safety-risk estimation aims to provide guidance of the train’s safe operation for communication-based train control system (CBTC) system, which is vital for hazards avoiding. In this paper, we present a novel intelligent-prediction model of safety-risk for CBTC system to predict which kind of risk state will happen under a certain operation condition. This model takes advantages of popular deep learning models, which is Deep Belief Networks (DBN). Some risk prediction factors is selected at first, and a critical function factor in CBTC system is generated by statistical model checking. Afterwards, for each input of samples, the model utilizes DBN to extract more condensed features, followed by a softmax layer to decouple the features further into different risk state. Through experiments on real-world dataset, we prove that our new proposed intelligent-prediction model outperforms traditional methods and demonstrate the effectiveness of the model in the safety-risk estimation for CBTC system.