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A Review of Road Traffic Accident Prediction Methods

Received: 20 April 2023     Accepted: 23 May 2023     Published: 29 May 2023
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Abstract

With the continuous development of urban traffic and the acceleration of urbanization, traffic accidents have become an important issue for urban safety and social stability. In order to prevent and reduce the occurrence of traffic accidents, traffic accident prediction technology has gradually become a hot spot for research. This paper analyzes road traffic accident prediction techniques from articles included in relevant English journals and provides a detailed introduction to the road traffic accident prediction techniques that are already in existence. This paper introduces the current status of research on traffic accident prediction techniques, including traditional statistical analysis methods, machine learning methods, neural network methods, time series analysis methods and techniques based on spatio-temporal data mining, and analyses the advantages and disadvantages of each road traffic accident prediction method. These methods are able to analyse the influencing factors of traffic accidents, build prediction models, improve prediction accuracy and provide strong support for road traffic accident prevention effects for urban traffic safety. Finally, the main difficulties faced in road traffic accident prediction and the future development trend of road traffic accident prediction is discussed. The work done in this paper can provide necessary theoretical support for relevant researchers and save the time needed for literature review.

Published in American Journal of Management Science and Engineering (Volume 8, Issue 3)
DOI 10.11648/j.ajmse.20230803.12
Page(s) 73-77
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Road Traffic Accidents, Prediction Models, Time Series Analysis

References
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[6] Molla, M. M., M. L. Stone and E. Lee, Identification of road traffic fatal crashes leading factors using principal components analysis. Journal of URISA, North Dakota State University, Fargo, ND (Under Review), 2015.
[7] Pourroostaei Ardakani, S., et al., Road Car Accident Prediction Using a Machine-Learning-Enabled Data Analysis. Sustainability, 2023. 15 (7): p. 5939.
[8] Yixuan, S., et al., Urban traffic accident time series prediction model based on combination of ARIMA and information granulation SVR. Journal of Tsinghua University (science and technology), 2014. 54 (3): p. 348--353.
[9] Yan, M. and Y. Shen, Traffic accident severity prediction based on random forest. Sustainability, 2022. 14 (3): p. 1729.
[10] Wenqi, L., L. Dongyu and Y. Menghua, A model of traffic accident prediction based on convolutional neural network. 2017, IEEE. p. 198--202.
[11] Shaik, M. E., M. M. Islam and Q. S. Hossain, A review on neural network techniques for the prediction of road traffic accident severity. Asian Transport Studies, 2021. 7: p. 100040.
[12] Ogwueleka, F. N., et al., An artificial neural network model for road accident prediction: a case study of a developing country. Acta Polytechnica Hungarica, 2014. 11 (5): p. 177--197.
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Cite This Article
  • APA Style

    Wang Shunshun, Yan Changshun, Shao Yong. (2023). A Review of Road Traffic Accident Prediction Methods. American Journal of Management Science and Engineering, 8(3), 73-77. https://doi.org/10.11648/j.ajmse.20230803.12

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    ACS Style

    Wang Shunshun; Yan Changshun; Shao Yong. A Review of Road Traffic Accident Prediction Methods. Am. J. Manag. Sci. Eng. 2023, 8(3), 73-77. doi: 10.11648/j.ajmse.20230803.12

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    AMA Style

    Wang Shunshun, Yan Changshun, Shao Yong. A Review of Road Traffic Accident Prediction Methods. Am J Manag Sci Eng. 2023;8(3):73-77. doi: 10.11648/j.ajmse.20230803.12

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  • @article{10.11648/j.ajmse.20230803.12,
      author = {Wang Shunshun and Yan Changshun and Shao Yong},
      title = {A Review of Road Traffic Accident Prediction Methods},
      journal = {American Journal of Management Science and Engineering},
      volume = {8},
      number = {3},
      pages = {73-77},
      doi = {10.11648/j.ajmse.20230803.12},
      url = {https://doi.org/10.11648/j.ajmse.20230803.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmse.20230803.12},
      abstract = {With the continuous development of urban traffic and the acceleration of urbanization, traffic accidents have become an important issue for urban safety and social stability. In order to prevent and reduce the occurrence of traffic accidents, traffic accident prediction technology has gradually become a hot spot for research. This paper analyzes road traffic accident prediction techniques from articles included in relevant English journals and provides a detailed introduction to the road traffic accident prediction techniques that are already in existence. This paper introduces the current status of research on traffic accident prediction techniques, including traditional statistical analysis methods, machine learning methods, neural network methods, time series analysis methods and techniques based on spatio-temporal data mining, and analyses the advantages and disadvantages of each road traffic accident prediction method. These methods are able to analyse the influencing factors of traffic accidents, build prediction models, improve prediction accuracy and provide strong support for road traffic accident prevention effects for urban traffic safety. Finally, the main difficulties faced in road traffic accident prediction and the future development trend of road traffic accident prediction is discussed. The work done in this paper can provide necessary theoretical support for relevant researchers and save the time needed for literature review.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - A Review of Road Traffic Accident Prediction Methods
    AU  - Wang Shunshun
    AU  - Yan Changshun
    AU  - Shao Yong
    Y1  - 2023/05/29
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ajmse.20230803.12
    DO  - 10.11648/j.ajmse.20230803.12
    T2  - American Journal of Management Science and Engineering
    JF  - American Journal of Management Science and Engineering
    JO  - American Journal of Management Science and Engineering
    SP  - 73
    EP  - 77
    PB  - Science Publishing Group
    SN  - 2575-1379
    UR  - https://doi.org/10.11648/j.ajmse.20230803.12
    AB  - With the continuous development of urban traffic and the acceleration of urbanization, traffic accidents have become an important issue for urban safety and social stability. In order to prevent and reduce the occurrence of traffic accidents, traffic accident prediction technology has gradually become a hot spot for research. This paper analyzes road traffic accident prediction techniques from articles included in relevant English journals and provides a detailed introduction to the road traffic accident prediction techniques that are already in existence. This paper introduces the current status of research on traffic accident prediction techniques, including traditional statistical analysis methods, machine learning methods, neural network methods, time series analysis methods and techniques based on spatio-temporal data mining, and analyses the advantages and disadvantages of each road traffic accident prediction method. These methods are able to analyse the influencing factors of traffic accidents, build prediction models, improve prediction accuracy and provide strong support for road traffic accident prevention effects for urban traffic safety. Finally, the main difficulties faced in road traffic accident prediction and the future development trend of road traffic accident prediction is discussed. The work done in this paper can provide necessary theoretical support for relevant researchers and save the time needed for literature review.
    VL  - 8
    IS  - 3
    ER  - 

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Author Information
  • Faculty of information Technology, Beijing University of Technology, Beijing, China

  • Faculty of information Technology, Beijing University of Technology, Beijing, China

  • Faculty of information Technology, Beijing University of Technology, Beijing, China

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