IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i24p9505-d464467.html
   My bibliography  Save this article

Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data

Author

Listed:
  • Seolyoung Lee

    (Research Institute of Engineering Technology, Hanyang University Erica Campus, Ansan 15588, Korea)

  • Jae Hun Kim

    (Research Institute of Engineering Technology, Hanyang University Erica Campus, Ansan 15588, Korea)

  • Jiwon Park

    (Department of Transportation and Logistics Engineering, Hanyang University Erica Campus, Ansan 15588, Korea)

  • Cheol Oh

    (Department of Transportation and Logistics Engineering, Hanyang University Erica Campus, Ansan 15588, Korea)

  • Gunwoo Lee

    (Department of Transportation and Logistics Engineering, Hanyang University Erica Campus, Ansan 15588, Korea)

Abstract

Background: Factors related to the wellness of taxi drivers are important for identifying high-risk drivers based on human factors. The purpose of this study is to predict high-risk taxi drivers based on a deep learning method by identifying the wellness of a driver, which reflects the personal characteristics of the driver. Methods: In-depth interviews with taxi drivers are conducted to collect wellness data. The priorities of factors affecting the severity of accidents are derived through a random forest model. In addition, based on the derived priority of variables, various combinations of inputs are set as scenarios and optimal artificial neural network models are derived for each scenario. Finally, the model with the best performance for predicting high-risk taxi drivers is selected based on three criteria. Results: A model with variables up to the 16th priority as inputs is selected as the best model; this has a classification accuracy of 86% and an F1-score of 0.77. Conclusions: The wellness-based model for predicting high-risk taxi drivers presented in this study can be used for developing a taxi driver management system. In addition, it is expected to be useful when establishing customized traffic safety improvement measures for commercial vehicle drivers.

Suggested Citation

  • Seolyoung Lee & Jae Hun Kim & Jiwon Park & Cheol Oh & Gunwoo Lee, 2020. "Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data," IJERPH, MDPI, vol. 17(24), pages 1-13, December.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:24:p:9505-:d:464467
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/24/9505/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/24/9505/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Javadreza Vahedi & Afshin Shariat Mohaymany & Zahra Tabibi & Milad Mehdizadeh, 2018. "Aberrant Driving Behaviour, Risk Involvement, and Their Related Factors Among Taxi Drivers," IJERPH, MDPI, vol. 15(8), pages 1-17, August.
    Full references (including those not matched with items on IDEAS)

    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. Xiao Lin & Zhengfeng Huang & Yun Ye & Jingxin Dong & Hongxiang Feng & Pengjun Zheng, 2023. "Effects of Aging on Taxi Service Performance: A Comparative Study Based on Different Age Groups," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    2. Chien-Hung Wei & Ying Lee & Yu-Wen Luo & Jyun-Jie Lu, 2021. "Incorporating Personality Traits to Assess the Risk Level of Aberrant Driving Behaviors for Truck Drivers," IJERPH, MDPI, vol. 18(9), pages 1-18, April.
    3. Quy Nguyen-Phuoc, Duy & An Ngoc Nguyen, Nguyen & Nguyen, Minh Hieu & Ngoc Thi Nguyen, Ly & Oviedo-Trespalacios, Oscar, 2022. "Factors influencing road safety compliance among food delivery riders: An extension of the job demands-resources (JD-R) model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 541-556.
    4. Nuria Gamero & Inmaculada Silla & Rubén Sainz-González & Beatriz Sora, 2018. "The Influence of Organizational Factors on Road Transport Safety," IJERPH, MDPI, vol. 15(9), pages 1-8, September.
    5. Muhammad Zahid & Yangzhou Chen & Sikandar Khan & Arshad Jamal & Muhammad Ijaz & Tufail Ahmed, 2020. "Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?," IJERPH, MDPI, vol. 17(11), pages 1-21, June.

    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:gam:jijerp:v:17:y:2020:i:24:p:9505-:d:464467. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.