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Edge Computing-Enabled Train Fusion Positioning: Modeling and Analysis

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Izdano u:Mathematics vol. 13, no. 6 (2025), p. 1015
Glavni autor: Yin, Hao
Daljnji autori: Song, Haifeng, Wu, Ruichao, Zhou, Min, Deng, Zixing, Dong, Hairong
Izdano:
MDPI AG
Teme:
Accuracy
Petri nets
Trains
Data processing
Collaboration
Edge computing
Vision systems
Communication
Modelling
Sensors
Onboard data processing
Onboard equipment
Algorithms
Data integration
Control systems
Tracking
Real time
Monitoring systems
Performance evaluation
Data transmission
Multisensor fusion
Computer simulation
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Sažetak:For train control systems, the accuracy of positioning tracking is essential for ensuring the safety and efficiency of operations. Multi-source information fusion techniques can improve positioning accuracy, but the computational limitations of onboard equipment impede the real-time processing capabilities required by advanced information fusion algorithms. An innovative approach, which combines multi-sensor information fusion with edge computing, is proposed to reduce the computational load on onboard systems and accelerate data processing. Colored Petri Nets (CPNs) are utilized for the modeling and validation of the algorithm. State-space analysis is used to evaluate the functional safety of the proposed method. Numerical simulations are performed to identify the key factors affecting the train positioning method’s performance. These simulations also determine the minimal tracking interval required for effective operation under edge computing. The results show that the edge computing-based train fusion positioning method reduces data processing delays and improves positioning accuracy. This approach offers a practical solution for real-time and accurate train control systems.
ISSN:2227-7390
Digitalni identifikator objekta:10.3390/math13061015
Izvor:Engineering Database