Review on machine learning-based traffic flow prediction methods

YAO Jun-feng; HE Rui; SHI Tong-tong; WANG Ping; ZHAO Xiang-mo

doi:10.19818/j.cnki.1671-1637.2023.03.003

Volume 23 Issue 3

Jun. 2023

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Article Navigation > Journal of Traffic and Transportation Engineering > 2023 > 23(3): 44-67

YAO Jun-feng, HE Rui, SHI Tong-tong, WANG Ping, ZHAO Xiang-mo. Review on machine learning-based traffic flow prediction methods[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 44-67. doi: 10.19818/j.cnki.1671-1637.2023.03.003

Citation:

YAO Jun-feng, HE Rui, SHI Tong-tong, WANG Ping, ZHAO Xiang-mo. Review on machine learning-based traffic flow prediction methods[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 44-67. doi: 10.19818/j.cnki.1671-1637.2023.03.003

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Review on machine learning-based traffic flow prediction methods

doi: 10.19818/j.cnki.1671-1637.2023.03.003

YAO Jun-feng^{1, 2
,},
HE Rui³,
SHI Tong-tong³,
WANG Ping^{4
,
,},
ZHAO Xiang-mo^{1, 5
,}

1.
School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
2.
China Communications Information Technology Group Co., Ltd., Beijing 101399, China
3.
School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
4.
School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China
5.
School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, Shaanxi, China

Funds:

National Key Research and Development Program of China 2021YFC3001003

Science and Technology Plan Project of Guangdong Province 2017B030314076

More Information

Author Bio:
YAO Jun-feng(1978-), male, senior engineer, doctoral student, jtbyaojf@126.com

WANG Ping(1982-), female, associate professor, PhD, wangp358@mail.sysu.edu.cn

ZHAO Xiang-mo(1966-), male, professor, PhD, xmzhao@chd.edu.cn
Received Date: 2022-12-15
Available Online: 2023-07-07
Publish Date: 2023-06-25

Abstract

Abstract

The research status and development trend of macro traffic flow prediction of designated road sections and regional road network at home and abroad were analyzed by literature review, expert interview, and experimental scenario construction. Local section traffic flow prediction methods were summarized, including traditional machine learning, recurrent neural networks, and hybrid models. The characteristics of convolutional neural networks, graph neural networks, and fusion multi-factor networks were discussed.The principles, advantages, limitations, and application scenarios of the methods were explained. The types of existing scenario traffic datasets and the mainstream traffic datasets at home and abroad were summarized from the perspectives of sampling periods and collecting methods. Analysis results show that recurrent neural networks can effectively obtain the historical laws of traffic data, but there are some problems such as gradient explosion, high computational complexity, and poor accuracy of long-time prediction. Graph neural networks introduce graph structures for road network topological connection relationships, which has obvious advantage in considering the spatiotemporal correlation of road network and traffic flow data. Fusion multi-factor methods fully consider the influence of internal and external factors such as weather, roads, and accidents, effectively improving the real-time performance and robustness of traffic flow prediction. The improvements of traffic flow prediction methods have limitations due to the difficult traffic data collection and external factor influence quantification, as well as the poor interpretability of machine learning methods. The future research should start from two aspects of starting the efficient mining of traffic information and the perfection of graph convolution methods, broaden the application of graph structures in the traffic field, and consider non-constant traffic scenarios. So as to further reveal the inherent laws of traffic data, develop more accurate and efficient traffic flow prediction methods, and promote the application of traffic flow prediction in industry.
- intelligent transportation system,
- traffic flow predication,
- machine learning,
- graph convolutional network,
- hybrid model,
- traffic dataset

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References(108)

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Review on machine learning-based traffic flow prediction methods

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