IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p2352-d1048641.html
   My bibliography  Save this article

Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal

Author

Listed:
  • Paulo Infante

    (CIMA, IIFA, University of Évora, 7000-671 Évora, Portugal
    Department of Mathematics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Gonçalo Jacinto

    (CIMA, IIFA, University of Évora, 7000-671 Évora, Portugal
    Department of Mathematics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Anabela Afonso

    (CIMA, IIFA, University of Évora, 7000-671 Évora, Portugal
    Department of Mathematics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Leonor Rego

    (Department of Mathematics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Pedro Nogueira

    (ICT, IIFA, University of Évora, 7000-671 Évora, Portugal
    Department of Geosciences, University of Évora, 7000-671 Évora, Portugal)

  • Marcelo Silva

    (ICT, IIFA, University of Évora, 7000-671 Évora, Portugal
    Department of Geosciences, University of Évora, 7000-671 Évora, Portugal)

  • Vitor Nogueira

    (Algoritmi Research Centre, University of Évora, 7000-671 Évora, Portugal
    Department of Informatics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • José Saias

    (Algoritmi Research Centre, University of Évora, 7000-671 Évora, Portugal
    Department of Informatics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Paulo Quaresma

    (Algoritmi Research Centre, University of Évora, 7000-671 Évora, Portugal
    Department of Informatics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Daniel Santos

    (Department of Informatics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Patrícia Góis

    (Department of Visual Arts and Design, EA, University of Évora, 7000-208 Évora, Portugal)

  • Paulo Rebelo Manuel

    (CIMA, IIFA, University of Évora, 7000-671 Évora, Portugal)

Abstract

Road traffic accidents (RTAs) are a problem with repercussions in several dimensions: social, economic, health, justice, and security. Data science plays an important role in its explanation and prediction. One of the main objectives of RTA data analysis is to identify the main factors associated with a RTA. The present study aims to contribute to the identification of the determinants for the type of RTA: collision, crash, or pedestrian running-over. These factors are essential for identifying specific countermeasures because there is a relevant relationship between the type of RTA and its severity. Daily RTA data from 2016 to 2019 in a district of Portugal were analyzed. A statistical multinomial logit model was fitted. The identified determinants for the type of RTA were geographical (municipality, location, and parking areas), meteorological (air temperature and weather), time of the day (hour, day of the week, and month), driver’s characteristics (gender and age), vehicle’s features (type and age) and road characteristics (road layout and type). The multinomial model results were compared with several machine learning algorithms, since the original data of the type of RTA are severely imbalanced. All models showed poor performance. However, when combining these models with ROSE for class balancing, their performance improved considerably, with the random forest algorithm showing the best performance.

Suggested Citation

  • Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2352-:d:1048641
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/2352/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/3/2352/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Maria Rella Riccardi & Francesco Galante & Antonella Scarano & Alfonso Montella, 2022. "Econometric and Machine Learning Methods to Identify Pedestrian Crash Patterns," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    3. Yookyung Boo & Youngjin Choi, 2021. "Comparison of Prediction Models for Mortality Related to Injuries from Road Traffic Accidents after Correcting for Undersampling," IJERPH, MDPI, vol. 18(11), pages 1-14, May.
    4. Yikai Chen & Kai Wang & Mark King & Jie He & Jianxun Ding & Qin Shi & Changjun Wang & Pingfan Li, 2016. "Differences in Factors Affecting Various Crash Types with High Numbers of Fatalities and Injuries in China," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-12, July.
    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. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    2. Jie Zhao & Ji Chen & Damien Beillouin & Hans Lambers & Yadong Yang & Pete Smith & Zhaohai Zeng & Jørgen E. Olesen & Huadong Zang, 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    3. Piaopiao Chen & Agnès H. Michel & Jianzhi Zhang, 2022. "Transposon insertional mutagenesis of diverse yeast strains suggests coordinated gene essentiality polymorphisms," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    4. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    5. Banks, Jonathan & Rabbani, Arif & Nadkarni, Kabir & Renaud, Evan, 2020. "Estimating parasitic loads related to brine production from a hot sedimentary aquifer geothermal project: A case study from the Clarke Lake gas field, British Columbia," Renewable Energy, Elsevier, vol. 153(C), pages 539-552.
    6. Alexander Wettstein & Gabriel Jenni & Ida Schneider & Fabienne Kühne & Martin grosse Holtforth & Roberto La Marca, 2023. "Predictors of Psychological Strain and Allostatic Load in Teachers: Examining the Long-Term Effects of Biopsychosocial Risk and Protective Factors Using a LASSO Regression Approach," IJERPH, MDPI, vol. 20(10), pages 1-20, May.
    7. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    8. Daifeng Xiang & Gangsheng Wang & Jing Tian & Wanyu Li, 2023. "Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    9. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    10. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    11. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    12. Natalia Distefano & Salvatore Leonardi & Nilda Georgina Liotta, 2023. "Walking for Sustainable Cities: Factors Affecting Users’ Willingness to Walk," Sustainability, MDPI, vol. 15(7), pages 1-18, March.
    13. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    14. Vorapot Sapsirisavat & Wiriya Mahikul, 2021. "Drinking and Night-Time Driving May Increase the Risk of Severe Health Outcomes: A 5-Year Retrospective Study of Traffic Injuries among International Travelers at a University Hospital Emergency Cente," IJERPH, MDPI, vol. 18(18), pages 1-9, September.
    15. Marcos Rodrigues & Fermín Alcasena & Pere Gelabert & Cristina Vega‐García, 2020. "Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1762-1779, September.
    16. Giovanny Pillajo-Quijia & Blanca Arenas-Ramírez & Camino González-Fernández & Francisco Aparicio-Izquierdo, 2020. "Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    17. Francesco Sartor & Jonathan P. Moore & Hans-Peter Kubis, 2021. "Plasma Interleukin-10 and Cholesterol Levels May Inform about Interdependences between Fitness and Fatness in Healthy Individuals," IJERPH, MDPI, vol. 18(4), pages 1-19, February.
    18. Zander S. Venter & Adam Sadilek & Charlotte Stanton & David N. Barton & Kristin Aunan & Sourangsu Chowdhury & Aaron Schneider & Stefano Maria Iacus, 2021. "Mobility in Blue-Green Spaces Does Not Predict COVID-19 Transmission: A Global Analysis," IJERPH, MDPI, vol. 18(23), pages 1-12, November.
    19. G. Brooke Anderson & Keith W. Oleson & Bryan Jones & Roger D. Peng, 2018. "Classifying heatwaves: developing health-based models to predict high-mortality versus moderate United States heatwaves," Climatic Change, Springer, vol. 146(3), pages 439-453, February.
    20. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.

    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:15:y:2023:i:3:p:2352-:d:1048641. 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.