基于XGBoost算法的高速公路短时交通流量预测OA

Under the background of rapid urbanization,the smooth flow of highway traffic is vital to economic efficiency and public lives.Thus,among the complex and dynamic highway networks,rapid and precise prediction of traffic flow is a crucial prerequisite for real-time traffic management.However,due to the nonlinear and random variation of short-term traffic flow,the accurate prediction of traffic volumes has been confronting with significant challenges.In order to address these challenges,a short-term traffic flow prediction model was built based on the XGBoost algorithm,aimed at enhancing the accuracy of traffic flow forecasting.Because of the robust learning capabilities and exceptional generalization performance of the XGBoost algorithm,this model can more effectively capture the intricate patterns and regularities of traffic flow through learning from historical traffic data.In order to validate the accuracy and efficacy of the XGBoost model,a series of tests were conducted by ETC gantry data from a section of the Yongxiu-Wuning Expressway in Jiangxi Province,and compared the results with traditional models such as ARIMA,BP,GBDT,and Prophet.The experimental results show that the XGBoost model exhibits higher prediction accuracy compared with traditional prediction models.This advancement holds promise for providing more effective decision support to highway traffic management authorities,facilitating the optimization of traffic flow,the reduction of traffic congestion,and the enhancement of overall traffic operational efficiency.