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Determination of the Severity of Motorcycle and Tricycle Accidents in Nigeria

Received: 11 May 2020     Accepted: 27 May 2020     Published: 17 June 2020
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Abstract

Road traffic accidents are a very rampant issue causing injury, loss of lives and property worldwide. In this research, a system for determining the severity of motorcycle accidents in Lokoja Metropolis of Central Nigeria was developed. The research considered different areas that are highly prone to accidents in Lokoja. Although accidents cannot be totally avoided, through scientific analysis, their frequency and severity can be reduced. The methodology used in this research is Knowledge Discovery in Databases with the Decision Tree Algorithm as the soft computing technique used for analysis. Python programming language was used for the implementation. The dataset used was gotten from the Federal Road Safety Corps (FRSC) in Lokoja. After the training and testing of the dataset, we achieved an accuracy of 90.5%. The motorcycle accident severity prediction system developed could serve as a tool that can be used to cub the enormous challenges faced by FRSC in curtailing motorcycle accident.

Published in Advances in Applied Sciences (Volume 5, Issue 2)
DOI 10.11648/j.aas.20200502.14
Page(s) 41-48
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), 2020. Published by Science Publishing Group

Keywords

Severity, Motorcycle, Accident, Knowledge Discovery, Decision Tree

References
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Cite This Article
  • APA Style

    Terungwa Simon Yange, Oluoha Onyekwere, Malik Adeiza Rufai, Charity Ojochogwu Egbunu, Onyinyechukwu Rehoboth Ogboli. (2020). Determination of the Severity of Motorcycle and Tricycle Accidents in Nigeria. Advances in Applied Sciences, 5(2), 41-48. https://doi.org/10.11648/j.aas.20200502.14

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    ACS Style

    Terungwa Simon Yange; Oluoha Onyekwere; Malik Adeiza Rufai; Charity Ojochogwu Egbunu; Onyinyechukwu Rehoboth Ogboli. Determination of the Severity of Motorcycle and Tricycle Accidents in Nigeria. Adv. Appl. Sci. 2020, 5(2), 41-48. doi: 10.11648/j.aas.20200502.14

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    AMA Style

    Terungwa Simon Yange, Oluoha Onyekwere, Malik Adeiza Rufai, Charity Ojochogwu Egbunu, Onyinyechukwu Rehoboth Ogboli. Determination of the Severity of Motorcycle and Tricycle Accidents in Nigeria. Adv Appl Sci. 2020;5(2):41-48. doi: 10.11648/j.aas.20200502.14

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  • @article{10.11648/j.aas.20200502.14,
      author = {Terungwa Simon Yange and Oluoha Onyekwere and Malik Adeiza Rufai and Charity Ojochogwu Egbunu and Onyinyechukwu Rehoboth Ogboli},
      title = {Determination of the Severity of Motorcycle and Tricycle Accidents in Nigeria},
      journal = {Advances in Applied Sciences},
      volume = {5},
      number = {2},
      pages = {41-48},
      doi = {10.11648/j.aas.20200502.14},
      url = {https://doi.org/10.11648/j.aas.20200502.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aas.20200502.14},
      abstract = {Road traffic accidents are a very rampant issue causing injury, loss of lives and property worldwide. In this research, a system for determining the severity of motorcycle accidents in Lokoja Metropolis of Central Nigeria was developed. The research considered different areas that are highly prone to accidents in Lokoja. Although accidents cannot be totally avoided, through scientific analysis, their frequency and severity can be reduced. The methodology used in this research is Knowledge Discovery in Databases with the Decision Tree Algorithm as the soft computing technique used for analysis. Python programming language was used for the implementation. The dataset used was gotten from the Federal Road Safety Corps (FRSC) in Lokoja. After the training and testing of the dataset, we achieved an accuracy of 90.5%. The motorcycle accident severity prediction system developed could serve as a tool that can be used to cub the enormous challenges faced by FRSC in curtailing motorcycle accident.},
     year = {2020}
    }
    

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    T1  - Determination of the Severity of Motorcycle and Tricycle Accidents in Nigeria
    AU  - Terungwa Simon Yange
    AU  - Oluoha Onyekwere
    AU  - Malik Adeiza Rufai
    AU  - Charity Ojochogwu Egbunu
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    Y1  - 2020/06/17
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    T2  - Advances in Applied Sciences
    JF  - Advances in Applied Sciences
    JO  - Advances in Applied Sciences
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    PB  - Science Publishing Group
    SN  - 2575-1514
    UR  - https://doi.org/10.11648/j.aas.20200502.14
    AB  - Road traffic accidents are a very rampant issue causing injury, loss of lives and property worldwide. In this research, a system for determining the severity of motorcycle accidents in Lokoja Metropolis of Central Nigeria was developed. The research considered different areas that are highly prone to accidents in Lokoja. Although accidents cannot be totally avoided, through scientific analysis, their frequency and severity can be reduced. The methodology used in this research is Knowledge Discovery in Databases with the Decision Tree Algorithm as the soft computing technique used for analysis. Python programming language was used for the implementation. The dataset used was gotten from the Federal Road Safety Corps (FRSC) in Lokoja. After the training and testing of the dataset, we achieved an accuracy of 90.5%. The motorcycle accident severity prediction system developed could serve as a tool that can be used to cub the enormous challenges faced by FRSC in curtailing motorcycle accident.
    VL  - 5
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    ER  - 

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Author Information
  • Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria

  • Department of Computer Science, University of Nigeria, Nsukka, Nigeria

  • Department of Computer Science, Federal University, Lokoja, Lokoja, Nigeria

  • Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria

  • Department of Computer Science, Federal University, Lokoja, Lokoja, Nigeria

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