现代电子技术2025，Vol.48Issue(16)：61-66,6.DOI:10.16652/j.issn.1004-373x.2025.16.011
基于集成学习的交通事故严重程度预测
Traffic accident severity prediction based on ensemble learning
摘要
Abstract
In order to improve the performance of road traffic accident severity prediction models and analyze the impact of accident features on accident severity,a method of traffic accident severity prediction based on a double-layer Stacking model is proposed.The BSMOTE2 algorithm is used to balance the data and verify whether data balancing processing will have a positive impact on model prediction.The GBDT-RFECV algorithm is used for k-fold cross validation selection to complete the feature dimensionality reduction.A two-layer Stacking model is built.The first layer is composed of BiGRU and XGBoost,using time series features for BiGRU and static features for XGBoost for the preliminary prediction.The CatBoost model is used at the second layer and combined with the prediction results of the first layer for the final severity prediction.The research results indicate that the accuracy of the model,macro F1,and macro AUC have all improved significantly,indicating that data balance processing has a positive impact on model prediction.In comparison with KNN,BiGRU,RF,and XGBoost models,the proposed double-layer Stacking model can improve prediction accuracy by 5.45%,10.23%,1.78%,and 2.34%,respectively,the macro F1 value can be increased by 5.31%,9.91%,1.35%,and 1.92%,respectively,and the macro AUC can be increased by 11.13%,6.97%,2.13%,and 2.71%,respectively.The double-layer Stacking model can perform better than other models on multiple evaluation metrics.关键词
交通安全/交通事故预测/预测分析/集成学习/机器学习/深度学习/特征降维Key words
traffic safety/traffic accident severity/predictive analysis/ensemble learning/machine learning/deep learning/feature dimensionality reduction分类
信息技术与安全科学引用本文复制引用
贾现广,宋腾飞,吕英英..基于集成学习的交通事故严重程度预测[J].现代电子技术,2025,48(16):61-66,6.基金项目
国家自然科学基金项目(71961012) （71961012）
