Safety justification of train movement dynamic processes using evidence theory and reference models

Abstract The efficient solution to justify train movement safety is to analyze train movement situations via train operation knowledge and knowledge-based inference tools. In this paper, train operation knowledge is represented as train movement models and conditions, collectively called rule-based train movement reference models. The Dempster–Shafer (D–S) evidence theory is employed to infer the model and condition under which a train is running. Consequently, aberrant models and conditions, potentially endangering train operation safety, are identified in advance so that emergency measures can be taken to prevent train operation accidents. The mass function is defined as the approximation level of the train operation time interval within one block section of a railway line to that obtained from various reference models. The D–S theory is also applied to train movement dynamic processes to gradually identify train operation situations, using the combined section and process mass functions. The proposed inference approach using evidence theory and reference models (ETRM) qualitatively and quantitatively judges the rationalities of train operation control logic and variation tendencies. A case study to prevent the occurrence of the 7/23 railway accident in China demonstrates the validity of the proposed inference approach using ETRM. The analysis and inference centering on train movement situations can meanwhile diagnose the operation status of train onboard and ground control systems.