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Article: A Novel STFSA-CNN-GRU Hybrid Model for Short-Term Traffic Speed Prediction

TitleA Novel STFSA-CNN-GRU Hybrid Model for Short-Term Traffic Speed Prediction
Authors
Keywordsconvolutional neural network
Correlation
Data models
deep learning model
Feature extraction
gated recurrent unit
Intelligent transportation systems
Prediction algorithms
Predictive models
Roads
Short-term traffic speed prediction
spatial-temporal analysis.
Issue Date1-Apr-2023
PublisherInstitute of Electrical and Electronics Engineers
Citation
IEEE Transactions on Intelligent Transportation Systems, 2023, v. 24, n. 4, p. 3728-3737 How to Cite?
Abstract

Short-term traffic speed prediction is fundamental to intelligent transportation systems (ITS), and the accuracy of the model largely determines the performance of real-time traffic control and management. In this study, a short-term traffic speed prediction method based on the spatial-temporal analysis of traffic flow and a combined deep-learning model, and a hybrid spatial-temporal feature selection algorithm (STFSA) of a convolutional neural network–gated recurrent unit (CNN-GRU)) is initially developed. Specifically, the STFSA is firstly employed to reconstruct the spatial-temporal matrix of traffic speed based on temporal continuity and spatial characteristics, and then this matrix is considered as the input feature of the prediction model. After this, the nonlinear fitting ability of the CNN is adopted to extract deep features from the convolutional and pooling layers for model training. Finally, by combining the timing and long-range dependence of the captured data with the forward GRU and the reverse GRU, the accuracy of the prediction result is further improved. The validity of the proposed model can be verified by comparing the prediction results with the actual traffic data. Accordingly, in the case study, the performance is compared with various benchmark methods under the same prediction scenario, verifying the superiority of the proposed model.


Persistent Identifierhttp://hdl.handle.net/10722/328479
ISSN
2023 Impact Factor: 7.9
2023 SCImago Journal Rankings: 2.580
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMa, Changxi-
dc.contributor.authorZhao, Yongpeng-
dc.contributor.authorDai, Guowen-
dc.contributor.authorXu, Xuecai-
dc.contributor.authorWong, Sze Chun-
dc.date.accessioned2023-06-28T04:45:19Z-
dc.date.available2023-06-28T04:45:19Z-
dc.date.issued2023-04-01-
dc.identifier.citationIEEE Transactions on Intelligent Transportation Systems, 2023, v. 24, n. 4, p. 3728-3737-
dc.identifier.issn1524-9050-
dc.identifier.urihttp://hdl.handle.net/10722/328479-
dc.description.abstract<p>Short-term traffic speed prediction is fundamental to intelligent transportation systems (ITS), and the accuracy of the model largely determines the performance of real-time traffic control and management. In this study, a short-term traffic speed prediction method based on the spatial-temporal analysis of traffic flow and a combined deep-learning model, and a hybrid spatial-temporal feature selection algorithm (STFSA) of a convolutional neural network–gated recurrent unit (CNN-GRU)) is initially developed. Specifically, the STFSA is firstly employed to reconstruct the spatial-temporal matrix of traffic speed based on temporal continuity and spatial characteristics, and then this matrix is considered as the input feature of the prediction model. After this, the nonlinear fitting ability of the CNN is adopted to extract deep features from the convolutional and pooling layers for model training. Finally, by combining the timing and long-range dependence of the captured data with the forward GRU and the reverse GRU, the accuracy of the prediction result is further improved. The validity of the proposed model can be verified by comparing the prediction results with the actual traffic data. Accordingly, in the case study, the performance is compared with various benchmark methods under the same prediction scenario, verifying the superiority of the proposed model.<br></p>-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.ispartofIEEE Transactions on Intelligent Transportation Systems-
dc.subjectconvolutional neural network-
dc.subjectCorrelation-
dc.subjectData models-
dc.subjectdeep learning model-
dc.subjectFeature extraction-
dc.subjectgated recurrent unit-
dc.subjectIntelligent transportation systems-
dc.subjectPrediction algorithms-
dc.subjectPredictive models-
dc.subjectRoads-
dc.subjectShort-term traffic speed prediction-
dc.subjectspatial-temporal analysis.-
dc.titleA Novel STFSA-CNN-GRU Hybrid Model for Short-Term Traffic Speed Prediction-
dc.typeArticle-
dc.identifier.doi10.1109/TITS.2021.3117835-
dc.identifier.scopuseid_2-s2.0-85124190226-
dc.identifier.volume24-
dc.identifier.issue4-
dc.identifier.spage3728-
dc.identifier.epage3737-
dc.identifier.eissn1558-0016-
dc.identifier.isiWOS:000751476700001-
dc.identifier.issnl1524-9050-

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