基于云遗传算法优选的SVR交通量预测模型  

Urban Traffic Flow Forecasting Base on CCLGA Algorithm Optimizes Parameters of SVR

作　　者：康海贵[1] 莫仁杰[1] 李明伟[1] 

机构地区：[1]大连理工大学港口,海岸及近海工程国家重点实验室,大连116024

出　　处：《武汉理工大学学报（交通科学与工程版）》2013年第2期225-229,共5页Journal of Wuhan University of Technology（Transportation Science & Engineering）

基　　金：教育部博士点专项基金项目(批准号:200801411105);河南省交通厅科技计划项目(批准号:200912)资助

摘　　要：针对城市主干道交通流量的实时变化和波动性特点,利用支持向量回归机(support vectorregression,SVR)进行城市主干道短时交通流量预测.为了优选SVR模型参数,基于混沌logistic映射和云自适应机制对标准遗传算法进行改进,建立了基于混沌云自适应遗传算法(chaos clouds adaptive genetic algorithm,CCLGA)进行SVR参数优选的CCLGA-SVR城市主干道短时交通流量预测模型.综合考虑了短时交通量各个影响因素,结合实测数据进行了实证预测分析,仿真结果表明文中提出的预测模型精度较高,寻优速度较快,可有效应用于城市主干道短时交通流量预测.To deal with the real-time changes and volatility of city main road traffic flow, support vec- tor regression （SVR） is applied. To choose the appropriate parameters with best effect, this paper proposes an algorithm named CCLGA algorithm, in which logistic chaos variables and cloud-based a-daptive and genet CCLGA-SVR city model was made, than the existing flow. ic algorithm was integrated. By combining CCLGA algor main road short-time traffic flow prediction model. Finall contrastive analysis represents that this model has faster Slc models, and can be effectively applied to predict city main ithm with SVR we get y the verification of the eed and higher precision road short-term traffic

关 键 词：支持向量机 遗传算法 云自适应 混沌映射 交通量预测 

分 类 号：U491.14[交通运输工程—交通运输规划与管理]