A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay Prediction
Predicting the status of train delays, a complex and dynamic problem, is crucial for railway enterprises and passengers. This paper proposes a novel hybrid deep learning model composed of convolutional neural networks (CNN) and temporal convolutional networks (TCN), named the CNN + TCN model, for pr...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/8163062 |
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| _version_ | 1849435095350378496 |
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| author | Dawei Wang Jingwei Guo Chunyang Zhang |
| author_facet | Dawei Wang Jingwei Guo Chunyang Zhang |
| author_sort | Dawei Wang |
| collection | DOAJ |
| description | Predicting the status of train delays, a complex and dynamic problem, is crucial for railway enterprises and passengers. This paper proposes a novel hybrid deep learning model composed of convolutional neural networks (CNN) and temporal convolutional networks (TCN), named the CNN + TCN model, for predicting train delays in railway systems. First, we construct 3D data containing the spatiotemporal characteristics of real-world train data. Then, the CNN + TCN model employs a 3D CNN component, which is fed into the constructed 3D data to mine the spatiotemporal characteristics, and a TCN component that captures the temporal characteristics in railway operation data. Furthermore, the characteristic variables corresponding to the two components are selected. Finally, the model is evaluated by leveraging data from two railway lines in the United Kingdom. Numerical results show that the CNN + TCN model has greater accuracy and convergence performance in train delay prediction. |
| format | Article |
| id | doaj-art-57208c75bef34b8db58379569e7dee74 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-57208c75bef34b8db58379569e7dee742025-08-20T03:26:25ZengWileyAdvances in Civil Engineering1687-80942024-01-01202410.1155/2024/8163062A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay PredictionDawei Wang0Jingwei Guo1Chunyang Zhang2School of Electronics and Control EngineeringFaculty of BusinessLuoyang Vocational College of Culture and TourismPredicting the status of train delays, a complex and dynamic problem, is crucial for railway enterprises and passengers. This paper proposes a novel hybrid deep learning model composed of convolutional neural networks (CNN) and temporal convolutional networks (TCN), named the CNN + TCN model, for predicting train delays in railway systems. First, we construct 3D data containing the spatiotemporal characteristics of real-world train data. Then, the CNN + TCN model employs a 3D CNN component, which is fed into the constructed 3D data to mine the spatiotemporal characteristics, and a TCN component that captures the temporal characteristics in railway operation data. Furthermore, the characteristic variables corresponding to the two components are selected. Finally, the model is evaluated by leveraging data from two railway lines in the United Kingdom. Numerical results show that the CNN + TCN model has greater accuracy and convergence performance in train delay prediction.http://dx.doi.org/10.1155/2024/8163062 |
| spellingShingle | Dawei Wang Jingwei Guo Chunyang Zhang A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay Prediction Advances in Civil Engineering |
| title | A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay Prediction |
| title_full | A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay Prediction |
| title_fullStr | A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay Prediction |
| title_full_unstemmed | A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay Prediction |
| title_short | A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay Prediction |
| title_sort | novel hybrid deep learning model for complex systems a case of train delay prediction |
| url | http://dx.doi.org/10.1155/2024/8163062 |
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