Traffic accident severity prediction based on FHSPSO-RF
WANG Shujiao
HU Xuelong
TANG Lei
Abstract：To enhance the accuracy of traffic accident severity prediction,an FHSPSO-RF model is developed,where foraging habitat selection particle swarm optimization(FHSPSO)is used to optimize key hyperparameters of a random forest(RF).Using traffic accident data from Seattle,Washington,from January 2022 to February 2023,twelve features are selected.The synthetic minority oversampling technique is applied to increase the samples of severe-injury and fatal crashes and improve class balance.The FHSPSO-RF model is compared with support vector machine(SVM),K-nearest neighbors(KNN),and logistic regression(LR).Shapley additive explanations(SHAP)are used to interpret how each feature influences severity.Results indicate that after oversampling,the recall of severe-injury and fatal crashes increases significantly,and the FHSPSO-RF model achieves more balanced overall performance.The model attains higher accuracy,precision,recall,and F1 score than the three benchmarks,yielding the best severity prediction.Across all crash types,the numbers of injuries and vehicles are the most influential drivers with significant positive effects on severity.The number of pedestrians,high-impact collision types such as head-on crashes,and complex road environments such as intersections and ramps form key risk combinations for severe-injury and fatal crashes.Property-damage-only crashes are closely related to whether a parked roadside vehicle is struck.The FHSPSO-RF model demonstrates strong predictive performance and interpretability,providing support for crash risk prediction and prevention-control decision making.
Keywords：traffic accidentseverity predictionFHSPSORF
Publication Date：2026-01-31
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：9( 25-33 )