IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i18p10239-d634936.html
   My bibliography  Save this article

Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

Author

Listed:
  • Farbod Farhangi

    (Geoinformation Tech. Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19697, Iran)

  • Abolghasem Sadeghi-Niaraki

    (Geoinformation Tech. Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19697, Iran
    Department of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Korea)

  • Seyed Vahid Razavi-Termeh

    (Geoinformation Tech. Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19697, Iran)

  • Soo-Mi Choi

    (Department of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Korea)

Abstract

Drivers’ lack of alertness is one of the main reasons for fatal road traffic accidents (RTA) in Iran. Accident-risk mapping with machine learning algorithms in the geographic information system (GIS) platform is a suitable approach for investigating the occurrence risk of these accidents by analyzing the role of effective factors. This approach helps to identify the high-risk areas even in unnoticed and remote places and prioritizes accident-prone locations. This paper aimed to evaluate tuned machine learning algorithms of bagged decision trees (BDTs), extra trees (ETs), and random forest (RF) in accident-risk mapping caused by drivers’ lack of alertness (due to drowsiness, fatigue, and reduced attention) at a national scale of Iran roads. Accident points and eight effective criteria, namely distance to the city, distance to the gas station, land use/cover, road structure, road type, time of day, traffic direction, and slope, were applied in modeling, using GIS. The time factor was utilized to represent drivers’ varied alertness levels. The accident dataset included 4399 RTA records from March 2017 to March 2019. The performance of all models was cross-validated with five-folds and tree metrics of mean absolute error, mean squared error, and area under the curve of the receiver operating characteristic (ROC-AUC). The results of cross-validation showed that BDT and RF performance with an AUC of 0.846 were slightly more accurate than ET with an AUC of 0.827. The importance of modeling features was assessed by using the Gini index, and the results revealed that the road type, distance to the city, distance to the gas station, slope, and time of day were the most important, while land use/cover, traffic direction, and road structure were the least important. The proposed approach can be improved by applying the traffic volume in modeling and helps decision-makers take necessary actions by identifying important factors on road safety.

Suggested Citation

  • Farbod Farhangi & Abolghasem Sadeghi-Niaraki & Seyed Vahid Razavi-Termeh & Soo-Mi Choi, 2021. "Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale," Sustainability, MDPI, vol. 13(18), pages 1-25, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10239-:d:634936
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/18/10239/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/18/10239/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gang Tao & Huansheng Song & Jun Liu & Jiao Zou & Yanxiang Chen, 2016. "A traffic accident morphology diagnostic model based on a rough set decision tree," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(8), pages 751-758, November.
    2. Xie, Zhixiao & Yan, Jun, 2013. "Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach," Journal of Transport Geography, Elsevier, vol. 31(C), pages 64-71.
    3. Anna, Petrenko, 2016. "Мaркування готової продукції як складова частина інформаційного забезпечення маркетингової діяльності підприємств овочепродуктового підкомплексу," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 2(1), March.
    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. Fatemeh Sadat Hosseini & Myoung Bae Seo & Seyed Vahid Razavi-Termeh & Abolghasem Sadeghi-Niaraki & Mohammad Jamshidi & Soo-Mi Choi, 2023. "Geospatial Artificial Intelligence (GeoAI) and Satellite Imagery Fusion for Soil Physical Property Predicting," Sustainability, MDPI, vol. 15(19), pages 1-25, September.
    2. Tongqiang Ding & Lianxin Zhang & Jianfeng Xi & Yingjuan Li & Lili Zheng & Kexin Zhang, 2023. "Bus Fleet Accident Prediction Based on Violation Data: Considering the Binding Nature of Safety Violations and Service Violations," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    3. Azher Ibrahim Al-Taei & Ali Asghar Alesheikh & Ali Darvishi Boloorani, 2023. "Land Use/Land Cover Change Analysis Using Multi-Temporal Remote Sensing Data: A Case Study of Tigris and Euphrates Rivers Basin," Land, MDPI, vol. 12(5), pages 1-14, 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. Vivian Welch & Christine M. Mathew & Panteha Babelmorad & Yanfei Li & Elizabeth T. Ghogomu & Johan Borg & Monserrat Conde & Elizabeth Kristjansson & Anne Lyddiatt & Sue Marcus & Jason W. Nickerson & K, 2021. "Health, social care and technological interventions to improve functional ability of older adults living at home: An evidence and gap map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    2. Erkmen Giray Aslim, 2019. "The Relationship Between Health Insurance and Early Retirement: Evidence from the Affordable Care Act," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 45(1), pages 112-140, January.
    3. Nihan Akyelken, 2017. "Mobility-Related Economic Exclusion: Accessibility and Commuting Patterns in Industrial Zones in Turkey," Social Inclusion, Cogitatio Press, vol. 5(4), pages 175-182.
    4. Dreher, Axel & Fuchs, Andreas & Langlotz, Sarah, 2019. "The effects of foreign aid on refugee flows," European Economic Review, Elsevier, vol. 112(C), pages 127-147.
    5. Georg Feigl & Markus Marterbauer & Miriam Rehm & Matthias Schnetzer & Sepp Zuckerstätter & Lars Nørvang Andersen & Thea Nissen & Signe Dahl & Peter Hohlfeld & Benjamin Lojak & Achim Truger & Andrew Wa, 2016. "The Elusive Recovery," SciencePo Working papers Main hal-03459084, HAL.
      • Georg Feigl & Markus Marterbauer & Miriam Rehm & Matthias Schnetzer & Sepp Zuckerstätter & Lars Nørvang Andersen & Thea Nissen & Signe Dahl & Peter Hohlfeld & Benjamin Lojak & Achim Truger & Andrew Wa, 2016. "The Elusive Recovery," PSE-Ecole d'économie de Paris (Postprint) hal-03459084, HAL.
      • Georg Feigl & Markus Marterbauer & Miriam Rehm & Matthias Schnetzer & Sepp Zuckerstätter & Lars Nørvang Andersen & Thea Nissen & Signe Dahl & Peter Hohlfeld & Benjamin Lojak & Thomas Theobald & Achim , 2016. "The Elusive Recovery," PSE Working Papers hal-03612850, HAL.
      • Georg Feigl & Markus Marterbauer & Miriam Rehm & Matthias Schnetzer & Sepp Zuckerstätter & Lars Nørvang Andersen & Thea Nissen & Signe Dahl & Peter Hohlfeld & Benjamin Lojak & Achim Truger & Andrew Wa, 2016. "The Elusive Recovery," Post-Print hal-03459084, HAL.
      • Georg Feigl & Markus Marterbauer & Miriam Rehm & Matthias Schnetzer & Sepp Zuckerstätter & Lars Nørvang Andersen & Thea Nissen & Signe Dahl & Peter Hohlfeld & Benjamin Lojak & Thomas Theobald & Achim , 2016. "The Elusive Recovery," Working Papers hal-03612850, HAL.
      • Georg Feigl & Markus Marterbauer & Miriam Rehm & Matthias Schnetzer & Sepp Zuckerstätter & Lars Nørvang Andersen & Thea Nissen & Signe Dahl & Peter Hohlfeld & Benjamin Lojak & Thomas Theobald & Achim , 2016. "The Elusive Recovery," SciencePo Working papers Main hal-03612850, HAL.
      • Georg Feigl & Markus Marterbauer & Miriam Rehm & Matthias Schnetzer & Sepp Zuckerstätter & Lars Nørvang Andersen & Thea Nissen & Signe Dahl & Peter Hohlfeld & Benjamin Lojak & Thomas Theobald & Achim , 2016. "The Elusive Recovery," PSE-Ecole d'économie de Paris (Postprint) hal-03612850, HAL.
    6. Billari, Francesco C. & Giuntella, Osea & Stella, Luca, 2018. "Broadband internet, digital temptations, and sleep," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 58-76.
    7. Ekaterina Aleksandrova & Kristian Behrens & Maria Kuznetsova, 2020. "Manufacturing (co)agglomeration in a transition country: Evidence from Russia," Journal of Regional Science, Wiley Blackwell, vol. 60(1), pages 88-128, January.
    8. Werner Eichhorst & Ulf Rinne, 2017. "Digital Challenges for the Welfare State," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 18(04), pages 03-08, December.
    9. Grazzini, Jakob & Richiardi, Matteo G. & Tsionas, Mike, 2017. "Bayesian estimation of agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 26-47.
    10. Bruno Biais & Fany Declerck & Sophie Moinas, 2016. "Who supplies liquidity, how and when?," BIS Working Papers 563, Bank for International Settlements.
    11. Chen, Cheng & Senga, Tatsuro & Sun, Chang & Zhang, Hongyong, 2023. "Uncertainty, imperfect information, and expectation formation over the firm’s life cycle," Journal of Monetary Economics, Elsevier, vol. 140(C), pages 60-77.
    12. Julie Vinck & Idunn Brekke, 2019. "Gender and education inequalities in parental employment when having a young child with increased care needs: Belgium and Norway compared," Working Papers 1904, Herman Deleeck Centre for Social Policy, University of Antwerp.
    13. Alvarez, Camila H. & Evans, Clare Rosenfeld, 2021. "Intersectional environmental justice and population health inequalities: A novel approach," Social Science & Medicine, Elsevier, vol. 269(C).
    14. Michal Gluszak & Remigiusz Gawlik & Malgorzata Zieba, 2019. "Smart and Green Buildings Features in the Decision-Making Hierarchy of Office Space Tenants: An Analytic Hierarchy Process Study," Administrative Sciences, MDPI, vol. 9(3), pages 1-16, July.
    15. Shisong Jiang, 2021. "“When Paradigms Are Out of Place”: Embracing Eclecticism in Legal Scholarship by Academic Turns," Laws, MDPI, vol. 10(4), pages 1-16, October.
    16. Ilde Rizzo & Anna Mignosa (ed.), 2013. "Handbook on the Economics of Cultural Heritage," Books, Edward Elgar Publishing, number 14326.
    17. Giovanni Dosi & Marcelo Pereira & Andrea Roventini & Maria Enrica Virgillito, 2016. "The Effects of Labour Market Reforms upon Unemployment and Income Inequalities: an Agent Based Model," Working Papers hal-03459264, HAL.
    18. Stefano Bianchini & Giulio Bottazzi & Federico Tamagni, 2017. "What does (not) characterize persistent corporate high-growth?," Small Business Economics, Springer, vol. 48(3), pages 633-656, March.
    19. Natalia Danzer & Martin Halla & Nicole Schneeweis & Martina Zweimüller, 2022. "Parental Leave, (In)formal Childcare, and Long-Term Child Outcomes," Journal of Human Resources, University of Wisconsin Press, vol. 57(6), pages 1826-1884.
    20. Krzysztof Karbownik & Anthony Wray, 2019. "Long-Run Consequences of Exposure to Natural Disasters," Journal of Labor Economics, University of Chicago Press, vol. 37(3), pages 949-1007.

    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:jsusta:v:13:y:2021:i:18:p:10239-:d:634936. 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.