【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.更多还原

【Abstract】 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.更多还原