A traffic accident early warning model based on XGBOOST
Yanzhi Pang, Xiang Wang, Yang Tang, Gu Guobin, Jianqiu Chen, Bin Yang, Longtang Ning, Chun Bao, Shugong Zhou, Xiali Cao, Xuguang Wen · 2024
Our research is based on the XGBOOST algorithm, which aims to build a model that can provide accurate early warning for traffic accidents. Our model incorporates machine learning algorithms and deep learning frameworks with the aim of providing effective early warning information by accurately predicting road traffic accidents.XGBOOST, as an efficient implementation of gradient boosting decision trees, dramatically improves the model's generalization ability and training speed through regularization techniques and cluster parallelization. The model parameters are tuned using the GridSearchCV exhaustive search method to ensure the optimal parameter configuration. In the experiments, the early warning model is constructed and optimized by classifying the severity of accidents and fusing multiple types of data using Yulin traffic accident data from 2015 to 2021. Eventually, the model performed well in terms of classification accuracy and stability, especially in recognizing major accident categories, with significantly higher recall and F1 scores. The study in this paper shows that the application of XGBOOST model in traffic accident early warning has high predictive performance and practical value, which provides important technical support for road safety.