An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree

High-speed train delay prediction has always been one of the important research issues in the railway dispatching. Accurate and interpretable delay prediction can enable staff to implement preventive measures and scheduling decisions in advance, and guide relevant departments to cooperate in complet...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2023-02, Vol.31 (2), p.421-433
Main Authors: Zhang, Dalin, Xu, Yi, Peng, Yunjuan, Du, Chenyue, Wang, Nan, Tang, Mincong, Lu, Lingyun, Liu, Jiqiang
Format: Article
Language:English
Subjects:
Artificial neural networks
Data models
Decision trees
Delay
Delays
Error reduction
Fuzzy decision tree
Fuzzy logic
graph community structure
graph convolutional network
High speed rail
high-speed train station delay prediction
interpretability
Multiple objective analysis
Neural networks
Optimization
Prediction algorithms
Prediction models
Predictive models
Rail transportation
Railway stations
Spatiotemporal phenomena
Task complexity
Time series
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Summary:High-speed train delay prediction has always been one of the important research issues in the railway dispatching. Accurate and interpretable delay prediction can enable staff to implement preventive measures and scheduling decisions in advance, and guide relevant departments to cooperate in completing complex transportation tasks, so as to improve rail transit operations, service quality, and the efficiency of train operation. This article proposes a new interpretable model based on graph community neural network and time-series fuzzy decision tree. This model can well capture the influence of spatiotemporal characteristics, train community structure, and multifactor in high-speed train station delay prediction. Besides, the time series fuzzy decision tree based on multiobjective optimization and reduced error pruning can mine potential decision rules to improve the model's interpretability, transparency, and high reliability. Finally, we prove that the prediction effect of the proposed model is superior than the other seven state-of-the-art models and our model is interpretable.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2022.3181453