Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model  

基于混沌云粒子群算法和PPPR模型的高速公路交通量预测(英文)

作　　者：赵泽辉[1] 康海贵[1] 李明伟[1] 

机构地区：[1]大连理工大学建设工程学部,大连116024

出　　处：《Journal of Southeast University(English Edition)》2013年第3期328-335,共8页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.71101014,50679008);Specialized Research Fund for the Doctoral Program of Higher Education(No.200801411105);the Science and Technology Project of the Department of Communications of Henan Province(No.2010D107-4)

摘　　要：Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.针对高速公路短时交通量实时性、波动性和非线性的特点,将参数投影寻踪回归(parameter projection pursuit regression,PPPR)方法应用于高速公路短时交通量预测.采用可变阶的正交Hermite多项式拟合其中的岭函数,运用最小二乘法确定多项式权系数c.为了更好地优选PPPR模型的投影方向a和岭函数个数M,利用混沌云粒子群算法对模型参数进行优选.提出了在外层优化岭函数个数M的同时,利用CCPSO算法在内层优化最佳投影方向a的CCPSO-PPPR混合优化高速公路短时交通量预测模型.将路段前几个时段交通量、天气因素和出行日期作为影响因素输入.实例预测与模型对比结果表明,该模型取得了更好的预测效果,绝对误差控制在[-6,6]以内,可有效应用于高速公路短时交通量预测.

关 键 词：expressway traffic flow forecasting projectionpursuit regression particle swarm algorithm chaoticmapping cloud model 

分 类 号：U491[交通运输工程—交通运输规划与管理]