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Road Traffic Accidents in Morocco: Exploratory Analysis of Driver, Vehicle, and Pedestrian Factors

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Abstract

Road safety is a major concern for both personal safety and public health in Morocco. The Ministry of Equipment, Transport, Logistics, and Water (METLW) reported that in 2017, there were 89,375 personal injuries, with an average of 361 injuries and 10 fatalities every day. The goal of this study is to evaluate Moroccan traffic accidents and identify the elements that affect drivers, vehicles, and pedestrians. We use a descriptive and exploratory statistical analysis on the Moroccan accident database between 2013 and 2017. METLW is the source of the database, which contains details about the characteristics of drivers, vehicles, and pedestrians. The majority of drivers, approximately 76%, are between the ages of 18 and 53, making them the group most at risk for car accidents. 95.2% of drivers are men, which is a significant amount. Because the majority of the victims’ drivers had very minor injuries or no injuries at all, intersections are where accidents are most likely to happen. Moroccan accident casualties and fatalities are not significantly influenced by vehicle type or use.

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Notes

  1. http://www.equipement.gov.ma/Transport-routier/Securite-routiere/Pages/Strategie-Nationale-de-la-securite-routiere-2017-20261009-7462.aspx.

  2. https://www.statisticssolutions.com/dissertation-resources/missing-values-in-data/.

  3. https://www.kaggle.com/rtatman/data-cleaning-challenge-handling-missing-values.

References

  1. Chen H, Cao L, Logan DB. Analysis of risk factors affecting the severity of intersection crashes by logistic regression. Traffic Inj Prev. 2012;13(3):300–7.

    Article  Google Scholar 

  2. Doğan AAE, Ngüngör AP. Estimating road accidents of Turkey based on regression analysis and artificial neural network approach. Adv Transp Stud. 2008;16:11–22.

    Google Scholar 

  3. Drissi TH, Faquir S, Yahyaouy A. Data mining techniques to analyze traffic accidents data: case application in morocco. In: Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), IEEE. 2020, pp. 1–4.

  4. Eboli L, Forciniti C. The severity of traffic crashes in Italy: an explorative analysis among different driving circumstances. Sustainability. 2020;12(3):856.

    Article  Google Scholar 

  5. El Gameh M, Echchelh A, Najih D, Touijer T, Chaouch A, Nekkal N, Soulaymani A. Quantitative analysis and study on the evolution of road safety in Morocco. Int J Res Stud Sci Eng Technol. 2014;1(9):210–5.

    Google Scholar 

  6. Ennajih D, Laaraifi A, Elgameh M, Sallik A, Chaouch A, Echchelh A. The quality management system on road safety in Morocco. Int J Res Stud Sci Eng Technol. 2018;5:1–6.

    Google Scholar 

  7. Janani G, Devi NR. Road traffic accidents analysis using data mining techniques. J Inf Technol Appl. 2016. https://doi.org/10.7251/JIT1702084J.

    Article  Google Scholar 

  8. Khyara H, Amine A, Nassih B. Exploratory analysis of driver and vehicle factors associated with traffic accidents in Morocco. In: Int Conf Intell Syst Pattern Recognit. Cham: Springer; 2022. p. 119–31.

    Google Scholar 

  9. Makaba T, Doorsamy W, Paul BS. Exploratory framework for analysing road traffic accident data with validation on Gauteng province data. Cogent Eng. 2020;7(1):1834659.

    Article  Google Scholar 

  10. Martin A, Lagarde E, Salmi LR. Burden of road traffic injuries related to delays in implementing safety belt laws in low-and lower-middle-income countries. Traffic Inj Prev. 2018;19(sup1):S1–6.

    Article  Google Scholar 

  11. Ospina-Mateus H, Jiménez LAQ, Lopez-Valdes FJ, Sana SS. Prediction of motorcyclist traffic crashes in Cartagena (Colombia): development of a safety performance function. RAIRO Oper Res. 2021;55(3):1257–78.

    Article  Google Scholar 

  12. Sapri FE, Nordin NS, Hasan SM, Yaacob WFW, Nasir SAM. Decision tree model for non-fatal road accident injury. Int J Adv Sci Eng Inf Technol. 2017;7(1):63–70.

    Article  Google Scholar 

  13. Sivasankaran SK, Balasubramanian V. Investigation of pedestrian crashes using multiple correspondence analysis in India. Int J Inj Control Saf Promot. 2020;27(2):144–55.

    Article  Google Scholar 

  14. Timmermans C, Alhajyaseen W, Al Mamun A, Wakjira T, Qasem M, Almallah M, Younis H. Analysis of road traffic crashes in the state of Qatar. Int J Inj Control Saf Promot. 2019;26(3):242–50.

    Article  Google Scholar 

  15. Tiwari G. Sustainable development goals and road traffic injuries: the new research challenge. 2017.

  16. Wachnicka J, Jamroz K. Speed, alcohol and safety belts as important factors influencing the number of fatalities in road accidents in the voivodships. J KONBIN. 2010;13(1):235–46.

    Article  Google Scholar 

  17. Wu Q, Zhang G, Ci Y, Wu L, Tarefder RA, Alcántara AD. Exploratory multinomial logit model-based driver injury severity analyses for teenage and adult drivers in intersection-related crashes. Traffic Inj Prev. 2016;17(4):413–22.

    Article  Google Scholar 

  18. Zhang G, Yau KK, Zhang X, Li Y. Traffic accidents involving fatigue driving and their extent of casualties. Acc Anal Prev. 2016;87:34–42.

    Article  Google Scholar 

  19. Zhang W, Zhang S. Chicago traffic collision data analysis based on multi-component analysis and exploratory data analysis. CICTP. 2020;2020:4684–96.

    Google Scholar 

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Acknowledgements

This work is part of the project “SafeRoad Meta-platform Road Safety (MSR)” which is supported by the METLW and the National Center of the Scientific and Technical Research (CNRST) under Contract No: 24/2017.

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  1. Ibn Tofail University, B.P 242, Kenitra, Morocco

    Hamza Khyara, Aouatif Amine & Bouchra Nassih

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  1. Hamza Khyara
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  2. Aouatif Amine
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  3. Bouchra Nassih
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Correspondence to Hamza Khyara.

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This article is part of the topical collection “Recent Trends on Machine Learning and Intelligent Systems” guest edited by Akram Bennour, Tolga Ensari, and Abdel-Badeeh Salem.

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Khyara, H., Amine, A. & Nassih, B. Road Traffic Accidents in Morocco: Exploratory Analysis of Driver, Vehicle, and Pedestrian Factors. SN COMPUT. SCI. 4, 101 (2023). https://doi.org/10.1007/s42979-022-01501-6

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