The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents (RTAs) on rural roads. Multiple Logistic Regression (MLR) was used to predict the likelihood of RTAs. For more accurate prediction, Multi-Layer Perceptron (MLP) and Radius Basis Function (RBF) neural networks were applied. Results indicated that in MLR, the model obtained from the backward method with the correct percent of 84.7% and R² value of 0.893 was the best method for predicting the likelihood of RTAs. Also, MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead, followed byand then vehicle-motorcycle/bike accidents were the greatest problems. Among the models, MLP had a better performance, so that the prediction accuracy of MLR, MLP, and RBF were 84.7%, 96.7%, and 92.1%, respectively. MLP model, due to higher accuracy, showed that the variable of reason of accident had the highest effect on the prediction of accidents, and considering MLR results, the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents. Therefore, motorcyclists and cyclists are more prone to accidents, and appropriate solutions should be adopted to enhance their safety.

本研究的目的是开发用于预测农村道路上道路交通事故 (RTA) 严重程度的统计和智能模型。多重逻辑回归 (MLR) 用于预测 RTA 的可能性。为了更准确的预测，应用了多层感知器 (MLP) 和半径基函数 (RBF) 神经网络。结果表明，在 MLR 中，从后向方法得到的模型正确率为 84.7%，R ²0.893 的值是预测 RTA 可能性的最佳方法。此外，MLR 显示，不注意前方不注意前方道路的变量，其次是车-摩托车/自行车事故是最大的问题。在这些模型中，MLP 的性能更好，MLR、MLP 和 RBF 的预测准确率分别为 84.7%、96.7% 和 92.1%。MLP模型由于精度较高，表明事故原因变量对事故预测的影响最大，考虑MLR结果，不注意前方、车-摩托车/自行车事故的变量有对事故发生的影响最大。因此，摩托车和骑自行车的人更容易发生事故，