A multi-Layer CNN-GRUSKIP model based on transformer for spatial −TEMPORAL traffic flow prediction
Traffic flow prediction remains a cornerstone for intelligent transportation systems (ITS), influencing both route optimization and environmental efforts. While Recurrent Neural Networks (RNN) and traditional Convolutional Neural Networks (CNN) offer some insights into the spatial–temporal dynamics...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447924004209 |
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| author | Karimeh Ibrahim Mohammad Ata Mohd Khair Hassan Ayad Ghany Ismaeel Syed Abdul Rahman Al-Haddad Thamer Alquthami Sameer Alani |
| author_facet | Karimeh Ibrahim Mohammad Ata Mohd Khair Hassan Ayad Ghany Ismaeel Syed Abdul Rahman Al-Haddad Thamer Alquthami Sameer Alani |
| author_sort | Karimeh Ibrahim Mohammad Ata |
| collection | DOAJ |
| description | Traffic flow prediction remains a cornerstone for intelligent transportation systems (ITS), influencing both route optimization and environmental efforts. While Recurrent Neural Networks (RNN) and traditional Convolutional Neural Networks (CNN) offer some insights into the spatial–temporal dynamics of traffic data, they’re often limited when navigating sparse and extended spatial–temporal patterns. In response, the CNN-GRUSKIP model emerges as a pioneering approach. Notably, it integrates the GRU-SKIP mechanism, a hybrid model that leverages the Gate Recurrent Unit’s (GRU) capabilities to process sequences with the ’SKIP’ feature’s ability to bypass and connect longer temporal dependencies, making it especially potent for traffic flow predictions with erratic and extended patterns. Another distinctive aspect is its non-standard 6-layer CNN, meticulously designed for in-depth spatiotemporal correlation extraction. The model comprises (1) the specialized CNN feature extraction, (2) the GRU-SKIP enhanced long-temporal module adept at capturing extended patterns, (3) a transformer module employing encoder-decoder and multi-attention mechanisms to hone prediction accuracy and trim model complexity, and (4) a bespoke prediction module. When tested against real-world datasets from California’s Caltrans Performance Measurement System (PeMS), specifically PeMS districts 4 and 8, the CNN-GRUSKIP consistently outperformed established models such as ARIMA, Graph Wave Net, HA, LSTM, STGCN, and APTN. With its potent predictive prowess and adaptive architecture, the CNN-GRUSKIP model stands to redefine ITS applications, especially where nuanced traffic dynamics are in play. |
| format | Article |
| id | doaj-art-1325b31f720e4ecfb85901e8210a30c0 |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-1325b31f720e4ecfb85901e8210a30c02024-12-18T08:48:13ZengElsevierAin Shams Engineering Journal2090-44792024-12-011512103045A multi-Layer CNN-GRUSKIP model based on transformer for spatial −TEMPORAL traffic flow predictionKarimeh Ibrahim Mohammad Ata0Mohd Khair Hassan1Ayad Ghany Ismaeel2Syed Abdul Rahman Al-Haddad3Thamer Alquthami4Sameer Alani5Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor Darul Ehsan, Malaysia; Corresponding authors.Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor Darul Ehsan, Malaysia; Corresponding authors.Computer Technology Engineering, College of Engineering Technology, Al-Kitab University, IraqDepartment of Computer and Communication Systems Engineering, Universiti Putra Malasia, Seri Kembangan, Selangor 43400, MalaysiaElectrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi ArabiaComputer Center, University of Anbar, IraqTraffic flow prediction remains a cornerstone for intelligent transportation systems (ITS), influencing both route optimization and environmental efforts. While Recurrent Neural Networks (RNN) and traditional Convolutional Neural Networks (CNN) offer some insights into the spatial–temporal dynamics of traffic data, they’re often limited when navigating sparse and extended spatial–temporal patterns. In response, the CNN-GRUSKIP model emerges as a pioneering approach. Notably, it integrates the GRU-SKIP mechanism, a hybrid model that leverages the Gate Recurrent Unit’s (GRU) capabilities to process sequences with the ’SKIP’ feature’s ability to bypass and connect longer temporal dependencies, making it especially potent for traffic flow predictions with erratic and extended patterns. Another distinctive aspect is its non-standard 6-layer CNN, meticulously designed for in-depth spatiotemporal correlation extraction. The model comprises (1) the specialized CNN feature extraction, (2) the GRU-SKIP enhanced long-temporal module adept at capturing extended patterns, (3) a transformer module employing encoder-decoder and multi-attention mechanisms to hone prediction accuracy and trim model complexity, and (4) a bespoke prediction module. When tested against real-world datasets from California’s Caltrans Performance Measurement System (PeMS), specifically PeMS districts 4 and 8, the CNN-GRUSKIP consistently outperformed established models such as ARIMA, Graph Wave Net, HA, LSTM, STGCN, and APTN. With its potent predictive prowess and adaptive architecture, the CNN-GRUSKIP model stands to redefine ITS applications, especially where nuanced traffic dynamics are in play.http://www.sciencedirect.com/science/article/pii/S2090447924004209Traffic Flow PredictionConvolutional Neural NetworkGRUSkip FunctionTransformerSpatial-temporal prediction |
| spellingShingle | Karimeh Ibrahim Mohammad Ata Mohd Khair Hassan Ayad Ghany Ismaeel Syed Abdul Rahman Al-Haddad Thamer Alquthami Sameer Alani A multi-Layer CNN-GRUSKIP model based on transformer for spatial −TEMPORAL traffic flow prediction Ain Shams Engineering Journal Traffic Flow Prediction Convolutional Neural Network GRU Skip Function Transformer Spatial-temporal prediction |
| title | A multi-Layer CNN-GRUSKIP model based on transformer for spatial −TEMPORAL traffic flow prediction |
| title_full | A multi-Layer CNN-GRUSKIP model based on transformer for spatial −TEMPORAL traffic flow prediction |
| title_fullStr | A multi-Layer CNN-GRUSKIP model based on transformer for spatial −TEMPORAL traffic flow prediction |
| title_full_unstemmed | A multi-Layer CNN-GRUSKIP model based on transformer for spatial −TEMPORAL traffic flow prediction |
| title_short | A multi-Layer CNN-GRUSKIP model based on transformer for spatial −TEMPORAL traffic flow prediction |
| title_sort | multi layer cnn gruskip model based on transformer for spatial temporal traffic flow prediction |
| topic | Traffic Flow Prediction Convolutional Neural Network GRU Skip Function Transformer Spatial-temporal prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2090447924004209 |
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