-Abrari Vajari, M. et al. (2020). A multinomial logit model of motorcycle crash severity at Australian intersections. Journal of Safety Research, 73, 17–24. doi.org/10.1016/j.jsr.2020.02.008
-Al-Moqri, T. et al. (2020). Exploiting Machine Learning Algorithms for Predicting Crash Injury Severity in Yemen: Hospital Case Study. Appl. Comput. Math, 9(5), 55–164.
-AlMamlook, R.E. et al. (2019). Comparison of machine learning algorithms for predicting traffic accident severity. in 2019 IEEE Jordan International Joint Conference On Electrical Engineering And Information Technology (JEEIT). IEEE,
272–276.
-Arhin, S.A. and Gatiba, A. (2020). Predicting crash injury severity at unsignalized intersections using support vector machines and naïve Bayes classifiers. Transportation Safety and Environment, 2(2), 120–132.
Behbahani, H., Effati, M. and Mortezaei, S. (2020). roviding a Method for Accident Severity Analysis Using Geospatial Clustering Functions and Decision Tree, Case Study: Qazvin-Loshan Freeway (in persian). Amirkabir J. Civil Eng., 52(6), 1419–1438.
-Hosseinzadeh, A., Moeinaddini, A. and Ghasemzadeh, A. (2021). Investigating factors affecting severity of large
truck-involved crashes: Comparison of the SVM and random parameter logit model. Journal of safety Research, 77, 151–160.
Iranian Legal Medicine Organization (1401). Available at: https://www.lmo.ir/.
-Kaplan, S. and Prato, C.G. (2012) .Risk factors associated with bus accident severity in the United States: A generalized ordered logit model. Journal of Safety Research, 43(3), 171–180. doi.org/10.1016/j.jsr.2012.05.003
-Labib, M.F. et al. (2019). Road accident analysis and prediction of accident severity by using machine learning in Bangladesh. in 2019 7th International Conference On Smart Computing & Communications (ICSCC). IEEE. 1–5.
-Santos, K., Dias, J.P. and Amado, C. (2022). A literature review of machine learning algorithms for crash injury severity prediction. Journal of Safety Research, 80, 254–269.
-Tavakoli Kashani, A. and Amirifar, S. (2020). Analyzing the effect of drivers’ characteristics on red-light running crash severity, case study: Isfahan (In persian). In The 18th International Conference on Traffic & Transportation. The 18th International Conference on Traffic & Transportation.
-Tavakoli Kashani, A. and Mohaymany, A.S. (2011). Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models’, Safety Science, 49(10), 1314–1320.doi.org/10.1016/j.ssci.2011.04.019
-Wang, X. and Kim, S.H. (2019). Prediction and factor identification for crash severity: comparison of discrete choice and tree-based models. Transportation Research Record, 2673(9), 640–653.
-World Health Organization (2021). extranet.who.int/roadsafety/death-on-the-roads/#country_or_area/IRN.
-Yuan, Y. et al. (2021). Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers. Journal of Safety Research, 76, 154–165.doi.org/10.1016/j.jsr.2020.12.012