Hybrid deep learning models for traffic prediction in large-scale road networks
Authors: Zheng, G., Chai, W.K., Duanmu, J.L. and Katos, V.
Journal: Information Fusion
Volume: 92
Pages: 93-114
ISSN: 1566-2535
DOI: 10.1016/j.inffus.2022.11.019
Abstract:Traffic prediction is an important component in Intelligent Transportation Systems(ITSs) for enabling advanced transportation management and services to address worsening traffic congestion problems. The methodology for traffic prediction has evolved significantly over the past decades from simple statistical models to recent complex integration of different deep learning models. In this paper, we focus on evaluating recent hybrid deep learning models in the task of traffic prediction. To this end, we first conducted a review and taxonomize the reviewed models based on their feature extraction methods. We analyze their constituent modules and architectural designs. We select ten models representative of different architectural choices from our taxonomy and conducted a performance comparison study. For this, we reconstruct the selected models and performed a series of comparative experiments under identical conditions with three well-known real-world datasets collected from large-scale road networks. We discuss the findings and insights based on our results, highlighting the differences in the achieved prediction accuracy by models with different design decisions.
https://eprints.bournemouth.ac.uk/37825/
Source: Scopus
Hybrid deep learning models for traffic prediction in large-scale road networks
Authors: Zheng, G., Chai, W.K., Duanmu, J.-L. and Katos, V.
Journal: INFORMATION FUSION
Volume: 92
Pages: 93-114
eISSN: 1872-6305
ISSN: 1566-2535
DOI: 10.1016/j.inffus.2022.11.019
https://eprints.bournemouth.ac.uk/37825/
Source: Web of Science (Lite)
Deep learning models for traffic prediction in large-scale road networks
Authors: Zheng, G., Chai, W.K., Duanmu, J.-L. and Katos, V.
Journal: Information Fusion
Publisher: Elsevier
ISSN: 1566-2535
https://eprints.bournemouth.ac.uk/37825/
Source: Manual
Hybrid deep learning models for traffic prediction in large-scale road networks.
Authors: Zheng, G., Chai, W.K., Duanmu, J.-L. and Katos, V.
Journal: Information Fusion
Volume: 92
Pages: 93-114
Publisher: Elsevier
ISSN: 1566-2535
Abstract:Traffic prediction is an important component in Intelligent Transportation Systems(ITSs) for enabling advanced transportation management and services to address worsening traffic congestion problems. The methodology for traffic prediction has evolved significantly over the past decades from simple statistical models to recent complex integration of different deep learning models. In this paper, we focus on evaluating recent hybrid deep learning models in the task of traffic prediction. To this end, we first conducted a review and taxonomize the reviewed models based on their feature extraction methods. We analyze their constituent modules and architectural designs. We select ten models representative of different architectural choices from our taxonomy and conducted a performance comparison study. For this, we reconstruct the selected models and performed a series of comparative experiments under identical conditions with three well-known real-world datasets collected from large-scale road networks. We discuss the findings and insights based on our results, highlighting the differences in the achieved prediction accuracy by models with different design decisions.
https://eprints.bournemouth.ac.uk/37825/
Source: BURO EPrints