一种优化的短时交通流量预测算法  

An optimized algorithm of short-term traffic flow prediction

作　　者：李诚[1] 张宏烈[1] 葛海淼[1] 杨欣宇[1] LI Cheng;ZHANG Honglie;GE Haimiao;YANG Xinyu(Schoolof Computer and Control Engineering,Qiqihar University,Qiqihar 161006,China)

机构地区：[1]齐齐哈尔大学计算机与控制工程学院,黑龙江齐齐哈尔161006

出　　处：《高师理科学刊》2020年第11期31-35,共5页Journal of Science of Teachers＇College and University

基　　金：黑龙江省省属高等学校基本科研业务费科研项目(135309463)。

摘　　要：短时交通流量预测是智能交通的理论基础,是交通流诱导系统中的关键性技术.对短时交通流量预测的研究具有很高的社会价值.目前,SVR智能预测模型已经被应用于这一领域.针对SVR模型参数选择难、预测精度有待提高等问题,运用人工蜂群算法(Artificial Bee Colony Algorithm,ABC)对SVR参数进行优化选择,提出了基于人工蜂群算法优化SVR的短时交通流量预测模型(ABC-SVR),并与其它典型预测模型进行了对比仿真实验.实验结果表明,ABC-SVR算法具有可行性和精确性.The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction system.The research on short-term traffic flow prediction has showed the considerable social value.At present,support vector regression(SVR)intelligent prediction model has been applied in this domain.Aiming at parameter selection difficulty and prediction accuracy improvement,the artificial bee colony algorithm(ABC)is adopted to optimize the SVR parameters.Accordingly,the short-term traffic flow prediction algorithm by support vector regression based on artificial bee colony optimization(ABC-SVR)is presented.The simulation experiments are carried out by comparing the ABC-SVR model with the other typical models,and the experimental results prove the feasibility and accuracy of the proposed ABC-SVR algorithm.

关 键 词：预测算法 人工蜂群算法 支持向量回归机 短时交通流量 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]