IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0214966.html
   My bibliography  Save this article

A comparative study on machine learning based algorithms for prediction of motorcycle crash severity

Author

Listed:
  • Lukuman Wahab
  • Haobin Jiang

Abstract

Motorcycle crash severity is under-researched in Ghana. Thus, the probable risk factors and association between these factors and motorcycle crash severity outcomes is not known. Traditional statistical models have intrinsic assumptions and pre-defined correlations that, if flouted, can generate inaccurate results. In this study, machine learning based algorithms were employed to predict and classify motorcycle crash severity. Machine learning based techniques are non-parametric models without the presumption of relationships between endogenous and exogenous variables. The main aim of this research is to evaluate and compare different approaches to modeling motorcycle crash severity as well as investigating the effect of risk factors on the injury outcomes of motorcycle crashes. Motorcycle crash dataset between 2011 and 2015 was extracted from the National Road Traffic Crash Database at the Building and Road Research Institute (BRRI) in Ghana. The dataset was classified into four injury severity categories: fatal, hospitalized, injured, and damage-only. Three machine learning based models were developed: J48 Decision Tree Classifier, Random Forest (RF) and Instance-Based learning with parameter k (IBk) were employed to model the severity of injury in a motorcycle crash. These machine learning algorithms were validated using 10-fold cross-validation technique. The three machine learning based algorithms were compared with one another and the statistical model: multinomial logit model (MNLM). Also, the relative importance analysis of the attribute was conducted to determine the impact of these attributes on injury severity outcomes. The results of the study reveal that the predictions of machine learning algorithms are superior to the MNLM in accuracy and effectiveness, and the RF-based algorithms show the overall best agreement with the experimental data out of the three machine learning algorithms, for its global optimization and extrapolation ability. Location type, time of the crash, settlement type, collision partner, collision type, road separation, road surface type, the day of the week, and road shoulder condition were found as the critical determinants of motorcycle crash injury severity.

Suggested Citation

  • Lukuman Wahab & Haobin Jiang, 2019. "A comparative study on machine learning based algorithms for prediction of motorcycle crash severity," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-17, April.
    • Handle: RePEc:plo:pone00:0214966
    DOI: 10.1371/journal.pone.0214966
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0214966
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0214966&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0214966?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yanyong Guo & Jibiao Zhou & Yao Wu & Zhibin Li, 2017. "Identifying the factors affecting bike-sharing usage and degree of satisfaction in Ningbo, China," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
    2. Yajie Zou & John E. Ash & Byung-Jung Park & Dominique Lord & Lingtao Wu, 2018. "Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1652-1669, July.
    3. Xin Ye & Ke Wang & Yajie Zou & Dominique Lord, 2018. "A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-17, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    2. Mohammed Abdul Muhsin Zambang & Haobin Jiang & Lukuman Wahab, 2021. "Modeling vehicle ownership with machine learning techniques in the Greater Tamale Area, Ghana," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-18, February.
    3. Aziemah Azhar & Noratiqah Mohd Ariff & Mohd Aftar Abu Bakar & Azzuhana Roslan, 2022. "Classification of Driver Injury Severity for Accidents Involving Heavy Vehicles with Decision Tree and Random Forest," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    4. Gholamreza Shiran & Reza Imaninasab & Razieh Khayamim, 2021. "Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison," Sustainability, MDPI, vol. 13(10), pages 1-23, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qian Duan & Xin Ye & Jian Li & Ke Wang, 2020. "Empirical Modeling Analysis of Potential Commute Demand for Carsharing in Shanghai, China," Sustainability, MDPI, vol. 12(2), pages 1-18, January.
    2. Changxi Ma & Dong Yang & Jibiao Zhou & Zhongxiang Feng & Quan Yuan, 2019. "Risk Riding Behaviors of Urban E-Bikes: A Literature Review," IJERPH, MDPI, vol. 16(13), pages 1-18, June.
    3. Chen Xu & Decun Dong & Dongxiu Ou & Changxi Ma, 2019. "Time-of-Day Control Double-Order Optimization of Traffic Safety and Data-Driven Intersections," IJERPH, MDPI, vol. 16(5), pages 1-18, March.
    4. Yajie Zou & Xinzhi Zhong & Jinjun Tang & Xin Ye & Lingtao Wu & Muhammad Ijaz & Yinhai Wang, 2019. "A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis," Sustainability, MDPI, vol. 11(2), pages 1-13, January.
    5. Yichuan Peng & Yuming Jiang & Jian Lu & Yajie Zou, 2018. "Examining the effect of adverse weather on road transportation using weather and traffic sensors," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-14, October.
    6. Xin Guan & Xin Ye & Cheng Shi & Yajie Zou, 2019. "A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China," Sustainability, MDPI, vol. 11(20), pages 1-19, October.
    7. Jingjing Xu & Behram Wali & Xiaobing Li & Jiaqi Yang, 2019. "Injury Severity and Contributing Driver Actions in Passenger Vehicle–Truck Collisions," IJERPH, MDPI, vol. 16(19), pages 1-16, September.
    8. Tomasz Bieliński & Łukasz Dopierała & Maciej Tarkowski & Agnieszka Ważna, 2020. "Lessons from Implementing a Metropolitan Electric Bike Sharing System," Energies, MDPI, vol. 13(23), pages 1-21, November.
    9. Ke Wang & Xin Ye, 2021. "Development of alternative stochastic frontier models for estimating time-space prism vertices," Transportation, Springer, vol. 48(2), pages 773-807, April.
    10. Muhammad Wisal Khattak & Hans De Backer & Pieter De Winne & Tom Brijs & Ali Pirdavani, 2024. "Comparative Evaluation of Crash Hotspot Identification Methods: Empirical Bayes vs. Potential for Safety Improvement Using Variants of Negative Binomial Models," Sustainability, MDPI, vol. 16(4), pages 1-22, February.
    11. Álvaro Aguilera-García & Juan Gomez & Natalia Sobrino & Juan José Vinagre Díaz, 2021. "Moped Scooter Sharing: Citizens’ Perceptions, Users’ Behavior, and Implications for Urban Mobility," Sustainability, MDPI, vol. 13(12), pages 1-26, June.
    12. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    13. Ruone Zhang & Xin Ye & Ke Wang & Dongjin Li & Jiayu Zhu, 2019. "Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
    14. Dandan Xu & Yang Bian & Shinan Shu, 2020. "Research on the Psychological Model of Free-floating Bike-Sharing Using Behavior: A Case Study of Beijing," Sustainability, MDPI, vol. 12(7), pages 1-18, April.
    15. Yan, Ying & Zhang, Ying & Yang, Xiangli & Hu, Jin & Tang, Jinjun & Guo, Zhongyin, 2020. "Crash prediction based on random effect negative binomial model considering data heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    16. Zhao, Chunkai & Wang, Yuhang & Ge, Zhenyu, 2023. "Is digital finance environmentally friendly in China? Evidence from shared-bike trips," Transport Policy, Elsevier, vol. 138(C), pages 129-143.
    17. Lixuan Zhao & Dewei Fang & Yang Cao & Shan Sun & Liu Han & Yang Xue & Qian Zheng, 2023. "Impact-Asymmetric Analysis of Bike-Sharing Residents’ Satisfaction: A Case Study of Harbin, China," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    18. Elżbieta Macioszek & Paulina Świerk & Agata Kurek, 2020. "The Bike-Sharing System as an Element of Enhancing Sustainable Mobility—A Case Study based on a City in Poland," Sustainability, MDPI, vol. 12(8), pages 1-29, April.
    19. Dandan Xu & Yang Bain & Shinan Shu & Xiaodong Zhang, 2022. "Staged Transition Process from Driving to Bicycling Based on the Effects of Latent Variables," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    20. Esther Fasan & Miles Tight & Harry Evdorides, 2021. "Factors Influencing Cycling among Secondary School Adolescents in an Ethnically Diverse City: The Perspective of Birmingham Transport Stakeholders," Sustainability, MDPI, vol. 13(22), pages 1-17, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0214966. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.