基于XGBoost算法的高速公路短时交通流量预测

Short-term Traffic Flow Prediction of Expressways by XGBoost Algorithm

在快速城市化背景下,高速公路交通流畅度对经济效率与民众生活至关重要,故在复杂多变的高速公路网中,快速精准预测交通流量成为实时交通管理的核心前提。然而,由于短时交通流具有非线性和随机变化的特点,交通流量的准确预测一直面临着巨大的挑战。为了克服这些挑战,构建了一种基于XGBoost算法的短时交通流量预测模型,旨在提高交通流量预测的准确性。该模型基于XGBoost算法的强大学习能力和优秀的泛化性能,通过对历史交通流量数据的学习,能够更好地捕捉交通流的复杂模式和规律。为了检验XGBoost模型的准确性和有效性,使用江西永武高速公路某路段ETC门架数据进行了一系列测试,并将结果与传统的ARIMA、BP、GBDT、Prophet模型进行了比较。实验结果表明,相比于传统的预测模型,XGBoost模型在短时交通流量预测中具有更高的预测精度。这将为公路交通管理部门提供更有效的决策支持,帮助其优化交通流,减少交通拥堵,提高交通运行效率。

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.