TY - GEN
T1 - Intelligent-Prediction Model of Safety-Risk for CBTC System by Deep Neural Network
AU - Zhang, Yan
AU - Liu, Jing
AU - Sun, Junfeng
AU - Chen, Xiang
AU - Zhou, Tingliang
N1 - Publisher Copyright:
© 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Communication-based train control system
KW - Deep learning
KW - Risk estimation
KW - Statistic model checking
UR - https://www.scopus.com/pages/publications/85077124540
U2 - 10.1007/978-3-030-30146-0_45
DO - 10.1007/978-3-030-30146-0_45
M3 - 会议稿件
AN - SCOPUS:85077124540
SN - 9783030301453
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 669
EP - 680
BT - Collaborative Computing
A2 - Wang, Xinheng
A2 - Gao, Honghao
A2 - Iqbal, Muddesar
A2 - Min, Geyong
PB - Springer
T2 - 15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019
Y2 - 19 August 2019 through 22 August 2019
ER -