基于免疫算法优化LSSVM的短时交通流预测  

Short-term traffic flow prediction based on LSSVM optimized by immune algorithm

作　　者：谷远利[1] 张源[1] 芮小平 陆文琦 李萌[1] 王硕[1] GU Yuan-li;ZHANG Yuan;RUI Xiao-ping;LU Wen-qi;LI Meng;WANG Shuo(Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Ministry of Transport,Beijing Jiaotong University,Beijing 100044,China;School of Earth Sciences and Engineering,Hohai University,Nanjing 210098,China)

机构地区：[1]北京交通大学综合交通运输大数据应用技术交通运输行业重点实验室,北京100044 [2]河海大学地球科学与工程学院,南京210098

出　　处：《吉林大学学报（工学版）》2019年第6期1852-1857,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目（41771478）;北京市科技计划项目（Z121100000312101）

摘　　要：为了智能化解决城市道路交通系统存在的问题,提高短时交通流预测的准确性,采用免疫算法优化的最小二乘支持向量机(LSSVM)建立短时交通流量预测模型。利用免疫算法对LSSVM中的惩罚因子和核函数参数进行优化,得到最优预测模型。以车辆行驶平均速度和占有率作为模型输入,交通流量作为输出进行预测仿真试验。试验结果表明:本文采用的优化LSSVM模型进行仿真试验的预测误差有所减小,输出结果更接近真实值。In order to intelligently solve the problems existing in urban road networks and improve the accuracy of short-term traffic flow prediction,a short-term traffic prediction model is established by using the least square support vector machine(LSSVM).Specifically,the immune algorithm is adopted to optimize the penalty factor and kernel parameters of the LSSVM,thus obtaining the optimal prediction model.The prediction simulation experiment takes the average speed and occupancy rate of vehicles as the input of the model to predict the traffic flow.The experimental results show that the prediction error of the optimized LSSVM model used in the simulation experiment is reduced,and the output result is closer to the real value.

关 键 词：交通运输系统工程 交通流预测 免疫算法 最小二乘支持向量机 参数优化 

分 类 号：U491[交通运输工程—交通运输规划与管理]