A hybrid machine learning approach for congestion prediction and warning

IF 1.3 4区 工程技术 Q3 TRANSPORTATION SCIENCE & TECHNOLOGY Transportation Planning and Technology Pub Date : 2024-06-27 DOI:10.1080/03081060.2024.2367751
Dongxue Li, Yao Hu, Chuliang Wu, Wangyong Chen, Feiyun Wang
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Abstract

Global traffic management encounters a significant challenge in traffic congestion. This paper presents a hybrid machine learning method for predicting traffic congestion. It leverages Convolutiona...
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全球交通管理面临着交通拥堵的巨大挑战。本文提出了一种预测交通拥堵的混合机器学习方法。该方法利用卷积法和...
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来源期刊
Transportation Planning and Technology
Transportation Planning and Technology 工程技术-运输科技
CiteScore
3.40
自引率
6.20%
发文量
24
审稿时长
12 months
期刊介绍: Transportation Planning and Technology places considerable emphasis on the interface between transportation planning and technology, economics, land use planning and policy. The Editor welcomes submissions covering, but not limited to, topics such as: • transport demand • land use forecasting • economic evaluation and its relationship to policy in both developed and developing countries • conventional and possibly unconventional future systems technology • urban and interurban transport terminals and interchanges • environmental aspects associated with transport (particularly those relating to climate change resilience and adaptation). The journal also welcomes technical papers of a more narrow focus as well as in-depth state-of-the-art papers. State-of-the-art papers should address transport topics that have a strong empirical base and contain explanatory research results that fit well with the core aims and scope of the journal.
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