Paper Info
Title
Train-movement situation recognition for safety justification using moving-horizon TBM-based multisensor data fusion
Abstract
Train-movement situation recognition is used in safety analyses to discern whether trains are running according to preset mechanisms. When a train deviates from the preset operation mechanisms, potential operation risks exist among the trains. Based on a transferable belief model (TBM), a mechanism of train-movement situation recognition is proposed in a rolling horizon through multisensor data fusion. The recognition procedure involves frame-of-discernment (FOD) definition, plausibility and belief calculation, pignistic probability calculation, and decision, which is applied to a dynamic process inference. The multisensor data fusion is formulated using the rolling-horizon TBM. The recognition mechanism permits the FOD cognition incompleteness and its discovery, which might involve the identification of aberrant dangerous situations. Using multiple positioning facilities, i.e., track circuits, balises, and global positioning systems, the risk prevention performance is verified through a train accident. The results demonstrate that the proposed recognition mechanism can correctly perceive train-movement situations and provide an early warning to avoid accident occurrences incurred by unfavorable train-movement situations.
Year
DOI
Venue
2019
10.1016/j.knosys.2019.04.010
Knowledge-Based Systems
Keywords
Field
DocType
Evidence theory,Transferable belief model,Safety analysis,Train movement pattern,Automatic trajectory analysis,Fault diagnosis
Pignistic probability,Warning system,Data mining,Computer science,Inference,Sensor fusion,Global Positioning System,Transferable belief model,Track circuit,Artificial intelligence,Train,Machine learning
Journal
Volume
ISSN
Citations 
177
0950-7051
2
PageRank 
References 
Authors
0.38
0
4
Name
Order
Citations
PageRank
Yonghua Zhou1396.27
Xin Tao283.87
Zhenyu Yu320.38
Hamido Fujita42644185.03