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Hybrid deep learning models for traffic prediction in large-scale road networks.

Zheng, G., Chai, W. K., Duanmu, J-L and Katos, V., 2023. Hybrid deep learning models for traffic prediction in large-scale road networks. Information Fusion, 92, 93-114.

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Zheng23-HybridModelsCompare (Accepted version).pdf - Accepted Version
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DOI: 10.1016/j.inffus.2022.11.019


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.

Item Type:Article
Uncontrolled Keywords:intelligent transportation system; traffic prediction; hybrid deep learning model; large-scale road networks
Group:Faculty of Science & Technology
ID Code:37825
Deposited By: Symplectic RT2
Deposited On:25 Nov 2022 11:50
Last Modified:24 May 2024 01:09


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