Predicting the severity of traffic accidents on mountain freeways with dynamic traffic and weather data

Abstract Traffic accident severity prediction is essential for dynamic traffic safety management. To explore the factors influencing the severity of traffic accidents on mountain freeways and to predict the severity of traffic accidents, four models based on machine learning algorithms are constructed using support vector machine (SVM), decision tree classifier (DTC), AdaSVM and AdaDTC. In addition, random forest (RF) is used to calculate the importance degree of variables and the accident severity influences with high importance levels form the RF dataset. The results show that rainfall intensity, collision type, number of vehicles involved in the accident and road section type are important variables influencing accident severity. The RF feature selection method improves the classification performance of four machine learning algorithms, resulting in a 9. 3%, 5. 5%, 7. 2% and 3. 6% improvement in prediction accuracy for SVM, DTC, AdaSVM and AdaDTC, respectively. The combination of the AdaSVM integrated algorithm and RF feature selection method has the best prediction performance, and it achieves 78. 9% and 88. 4% prediction precision and accuracy, respectively.