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Bayesian Spatio-Temporal Graph Convolutional Network for Railway Train Delay Prediction
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DOI：10.1109/TITS.2024.3409754.png)
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This study introduces a novel approach that integrates dynamic Bayesian network with attention based spatio-temporal graph convolutional network to forecast railway train delays, capturing the intricate operation interactions between train events and the dynamic evolution of train delays. Initially, train delay patterns are identified using the $K$ -means clustering algorithm and incorporated as additional variables into the prediction model. To capture dynamic causality in delay propagation, we utilize a dynamic Bayesian network-based dynamic causality graph, incorporating train delay data and domain knowledge to effectively model the train delay propagation. Leveraging these insights on delay dynamic propagation, we propose an attention based spatio-temporal graph convolutional network that effectively models the dynamic spatio-temporal dependency among train events and enhances the accuracy of delay predictions. The proposed method is assessed using operational data from the Wuhan-Guangzhou high-speed railway. Results show that the proposed model outperforms the baseline models, particularly with the expansion of the prediction horizon. The learned dynamic causality of the train delay propagation enhances interpretability and results in a 6.97% reduction in mean absolute error. Furthermore, train delay patterns and weather variables contribute to a respective 12.85% and 4.37% reduction in mean absolute error. The statistical tests further validate the efficacy of the proposed model.
Keyword:
Delays
Predictive models
Data models
Mathematical models
Rail transportation
Bayes methods
Analytical models
High-speed railway
train delay prediction
train delay pattern
dynamic Bayesian network
graph convolution network
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
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Train Delay Prediction Systems: A Big Data Analytics Perspective列车延误预测系统: 大数据分析视角.png)
BIG DATA RESEARCH
BIG DATA RESEARCHIF4.2
PREAI
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