安顺学院学报2024，Vol.26Issue(3)：131-136,6.
基于SMA-SVR模型的城市道路短时交通流预测
Short-Term Traffic Flow Prediction on Urban Roads Based on SMA-SVR Models
摘要
Abstract
Short-term traffic flow prediction is one of the key issues in the field of dynamic traffic control and management.Due to uncertainty and nonlinearity,short-term traffic flow prediction is still a challenging task.In order to improve the accuracy of short-time traffic flow prediction,this paper propo-ses a Support Vector Regression(SVR)model optimised based on Slime Mould Algorithm(SMA).the data of weekday morning and evening peak traffic flow at Donghai Avenue-Caoshan Road intersection in Bengbu City were collected,the penalty parameters and kernel function parameters of the SVR model are efficiently optimised using SMA,the SMA-SVR model is built for case validation.The results show that the SMA-SVM model has the highest prediction accuracy,i.e.R2=0.97054,RMSE=47.7826,MAPE=7.1703％,and the fastest iterative convergence speed compared with the original SVR model and the SVR model based on the Particle Swarm Optimisation algorithm and the Sparrow Search algorithm.It can be seen that the proposed SMA-SVM model can be used for short-term traffic flow prediction on urban roads.关键词
城市道路/短时交通流/支持向量回归模型/黏菌优化Key words
urban roads/short-term traffic flow/Support Vector Regression models/Slime Mould Optimisation分类
交通工程引用本文复制引用
岳鑫鑫,常山,马露,于敏,韩意..基于SMA-SVR模型的城市道路短时交通流预测[J].安顺学院学报,2024,26(3):131-136,6.基金项目
安徽省高校科学研究重大项目"考虑初始状态的水合物沉积物热-水-力耦合机理"(2023AH040274) （2023AH040274）
安徽省高校科学研究重点项目"正八边形腹梁受剪性能研究"(2023AH051841) （2023AH051841）
安徽省高校科学研究重点项目"极端环境下基于COOT算法及时变可靠度的混凝土结构耐久性研究"(2023AH051863) （2023AH051863）
安徽省高校优秀青年骨干教师国内访问研修项目"腐蚀环境下基于全寿命设计需求与时变可靠度的混凝土结构耐久性研究"(gxgnfx2022042). （gxgnfx2022042）
