Overview of machine learning-based traffic flow prediction

Zhibo Xing; Mingxia Huang; Dan Peng

doi:10.48130/DTS-2023-0013

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Digital Transportation and Safety ›› 2023, Vol. 2 ›› Issue (3) : 164-175. DOI: 10.48130/DTS-2023-0013

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Overview of machine learning-based traffic flow prediction

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Abstract

Traffic flow prediction is an important component of intelligent transportation systems. Recently, unprecedented data availability and rapid development of machine learning techniques have led to tremendous progress in this field. This article first introduces the research on traffic flow prediction and the challenges it currently faces. It then proposes a classification method for literature, discussing and analyzing existing research on using machine learning methods to address traffic flow prediction from the perspectives of the prediction preparation process and the construction of prediction models. The article also summarizes innovative modules in these models. Finally, we provide improvement strategies for current baseline models and discuss the challenges and research directions in the field of traffic flow prediction in the future.

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Traffic flow prediction / Machine learning / Intelligent transportation / Deep learning

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Zhibo Xing, Mingxia Huang, Dan Peng. Overview of machine learning-based traffic flow prediction. Digital Transportation and Safety, 2023, 2(3): 164‒175 https://doi.org/10.48130/DTS-2023-0013

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This work was supported by 2022 Shenyang Philosophy and Social Science Planning under grant SY202201Z, Liaoning Provincial Department of Education Project under grant LJKZ0588.