Shahrood University of Technology Journal of AI and Data Mining 2322-5211 12 4 2024 11 01 Application of Machine Learning and Metaheuristic Optimizer Algorithm for Crash Severity Prediction in the Urban Road Network 521 534 3345 10.22044/jadm.2024.15103.2614 EN Morteza Mohammadi Zanjireh Computer Engineering Department, Imam Khomeini International University, Qazvin, Iran Farzad Morady Civil Engineering Department, Imam Khomeini International University, Qazvin, Iran Journal Article 2024 09 26 This paper predicts the severity of crashes based on the analysis of multiple variables and using machine learning methods. For this purpose, data related to the years 2012 to 2024 of Tempe city in the state of Arizona USA was used. Features were selected using the metaheuristic method. Then, by using decision tree and artificial neural network, the classification of the severity of crashes was carried out. Based on the metrics, decision tree with an overall accuracy of 54% was the optimal. Finally, using the permutation feature importance method, the optimal model was interpreted. The results show that the characteristics of the year with 0.22 and the spatial characteristics with 0.11 and the collision manner with 0.1 have a higher importance in predicting the severity of crashes on urban roads. https://jad.shahroodut.ac.ir/article_3345_99bd75235fae9ec03a89411c5b39f5a6.pdf