Abstract
To further strengthen road traffic safety management and improve the accuracy of early warning systems for road traffic safety, a model for factors causing urban road traffic accidents based on the XGBoost algorithm is proposed. Firstly, SMOTE is used to process the unbalanced data, including supplementing the missing values, deleting the duplicate values, and visualizing the data. Then the prediction model is built by the XGBoost algorithm. By comparing and analyzing the results with the LR model, linear SVM model, DT model and Lightgbm model, the average accuracy rate of the XGBoost model reaches 0.979. Based on the XGBoost algorithm, the analysis of the factors causing urban road traffic accidents has better prediction performance, which can provide a reliable reference for preventing traffic accidents.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
