Does gender affect the identification of road crash occurrences? An Overview and a comparative study


  • Soukaina EL FEROUALI Cadi Ayyad University
  • zouhair Elamrani Abou Elassad Cadi Ayyad University
  • Abdelmounaîm ABDALI Cadi Ayyad University


Road safety, Accident severity, Risk factors, Machine learning


Around the world, traffic accidents are regarded as a large and significant cause of injury and death. Nearly 3,700 people are killed and over 1.3 million individuals lose their lives in collisions involving trucks, vehicles, buses, motorcycles, or pedestrians. This article tries to identify the important causes of traffic accidents involving both men and women as well as the methods that have been suggested and put into practice based on the literature study. In order to determine how gender impacts the frequency of traffic accidents, a survey and a comparative study were conducted in this work. According to the findings, the factors that have been studied for accident causes in urban areas include speed, age, and gender. On rural roads, speed has been recognized as the primary cause of collisions, particularly among men, while age and lack of experience have been noted as influencing factors in women's traffic incidents. Because machine learning models are effective at predicting crashes, they have been utilized in the majority of research


Download data is not yet available.


S. Regev, J. J. Rolison, and S. Moutari, “Crash risk by driver age, gender, and time of day using a new exposure methodology,” J. Safety Res., vol. 66, pp. 131–140, 2018.

M. Mohanty and A. Gupta, “Factors affecting road crash modeling,” J. Transp. Lit., vol. 9, no. 2, pp. 15–19, 2015.

O. S. Liang and C. C. Yang, “How are different sources of distraction associated with at-fault crashes among drivers of different age gender groups?,” Accid. Anal. Prev., vol. 165, no. February 2021, p. 106505, 2022.

L. T. Truong, H. T. T. Nguyen, and R. Tay, “A random parameter logistic model of fatigue-related motorcycle crash involvement in Hanoi, Vietnam,” Accid. Anal. Prev., vol. 144, no. June, p. 105627, 2020.

X. Zhai, H. Huang, N. N. Sze, Z. Song, and K. K. Hon, “Diagnostic analysis of the effects of weather condition on pedestrian crash severity,” Accid. Anal. Prev., vol. 122, no. November 2018, pp. 318–324, 2019.

Z. Ma, S. I. J. Chien, C. Dong, D. Hu, and T. Xu, “Exploring factors af- fecting injury severity of crashes in freeway tunnels,” Tunn. Undergr. Sp. Technol., vol. 59, pp. 100–104, 2016.

J. J. Rolison, S. Regev, S. Moutari, and A. Feeney, “What are the factors that contribute to road accidents? An assessment of law en- forcement views, ordinary drivers’ opinions, and road accident records,” Accid. Anal. Prev., vol. 115, no. August 2017, pp. 11–24, 2018.

P. B. Silva, M. Andrade, and S. Ferreira, “Machine learning applied to road safety modeling: A systematic literature review,” J. Traffic Transp. Eng. (English Ed., vol. 7, no. 6, pp. 775–790, 2020.

N. Mor, H. Sood, and T. Goyal, “Application of machine learning technique for prediction of road accidents in Haryana-A novel ap- proach,” J. Intell. Fuzzy Syst., vol. 38, no. 5, pp. 6627–6636, 2020.

S. S. Yassin and Pooja, “Road accident prediction and model interpre- tation using a hybrid K-means and random forest algorithm approach,” SN Appl. Sci., vol. 2, no. 9, pp. 1–13, 2020.

A. Mohammed et al., “A Landscape of Research on Bus Driver Be- havior: Taxonomy, Open Challenges, Motivations, Recommendations, Limitations, and Pathways Solution in Future,” IEEE Access, vol. 9, pp. 139896–139927, 2021.

A. Abdulhafedh, “Road Crash Prediction Models: Different Statistical Modeling Approaches,” J. Transp. Technol., vol. 07, no. 02, pp. 190–205, 2017.

Z. Elamrani Abou Elassad and H. Mousannif, Understanding driving behavior: Measurement, modeling and analysis, vol. 915. Springer International Publishing, 2019.

C. Lin, D. Wu, H. Liu, X. Xia, and N. Bhattarai, “Factor identification and prediction for teen driver crash severity using machine learning: A case study,” Appl. Sci., vol. 10, no. 5, 2020.

S. Ul Hassan, J. Chen, A. A. Shah, and T. Mahmood, “Acci- dent detection and disaster response framework utilizing IoT,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 3, pp. 379–385, 2020.

H. Razi-Ardakani, A. Ariannezhad, and M. Kermanshah, “A study of sex differences on road crash severity,” Int. Conf. Civil, Struct. Transp. Eng., no. 113, pp. 113.1-113.14, 2018.

D. L. Massie, P. E. Green, and K. L. Campbell, “Crash involvement rates by driver gender and the role of average annual mileage,” Accid. Anal. Prev., vol. 29, no. 5, pp. 675–685, 1997.

S. Mafi, Y. AbdelRazig, and R. Doczy, “Machine Learning Methods to Analyze Injury Severity of Drivers from Different Age and Gender Groups,” Transp. Res. Rec., vol. 2672, no. 38, pp. 171–183, Dec. 2018.

C. Charbit, “Les facteurs humains dans les accidents de la circulation: un potentiel important pour des actions de pre´vention,” En Ligne], Www. Fond. Maif. Fr/ . . . , 1997, [Online].

“World Health Organization,WHO, (2015).”

M. A. Rahim and H. M. Hassan, “A deep learning based traffic crash severity prediction framework,” Accid. Anal. Prev., vol. 154, no. February, p. 106090, 2021.

L. Eboli, C. Forciniti, and G. Mazzulla, “Factors influencing accident severity: An analysis by road accident type,” Transp. Res. Procedia, vol. 47, pp. 449–456, 2020.

Z. Ma, G. Mei, and S. Cuomo, “An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors,” Accid. Anal. Prev., vol. 160, no. July, p. 106322, 2021.




How to Cite

EL FEROUALI, S., Elamrani Abou Elassad, zouhair, & ABDALI, A. (2023). Does gender affect the identification of road crash occurrences? An Overview and a comparative study. International Journal of Computer Engineering and Data Science (IJCEDS), 3(1), 8–12. Retrieved from

Similar Articles

You may also start an advanced similarity search for this article.