摘要
及时准确地进行交通流短时预测是智能运通系统(ITS),尤其是其先进的交通管理系统(ATM S)与先进的出行者信息系统(AT IS)研究的关键内容之一。随着预测时间跨度的缩短,交通流量的变化显示出越来越强的不确定性,使得一般方法的预测精度大大降低。例如:非参数回归的算法是一种“无参数”、可移植、高预测精度的实时预测算法,在交通流预测中发挥了很大的作用,但随着样本数据维数的增加,存在“维数祸根”的现象。针对目前短时交通流预测存在的问题,本文提出一种基于投影寻踪自回归技术的短时交通流预测模型,解决了“维数祸根”和高维数据间的非正态、非线性问题。经过实测数据验证,该算法完全满足实时交通流预测的需要。
Accurate short-time traffic flow forecasting is one of the important issues for Intelligent Transportation Systems research, especially for the Advanced Traffic Management Systems and Advanced Traveler Information Systems research. With the shortening of the forecasting term, the uncertainty of traffic flow becomes more and more ,seriously, so that the forecasting effect of general approaches is decreasing. For an example, the algorithm based on non-parametric regression is a real-time nonparametric forecasting algorithm with the characteristic of high transplantation and accuracy, which plays an important role in traffic flow forecasting, yet there is the problem of ＂dimension curse＂ as the dimensions of the sample data increase. For the purpose of solving the question of short-time traffic flow forecasting, a short-time traffic flow forecasting model based on projection pursuit auto regression technique is established in this paper. The problem of ＂dimension curse＂ and non-normality among high-dimensions data are silved. This algorithm satisfies the need of real-time traffic flow forecasting completely through the field data test.
出处
《系统工程》
CSCD
北大核心
2006年第3期20-24,共5页
Systems Engineering
基金
国家自然科学基金资助项目(50378042)
山东省社会科学规划研究项目(04CMZ08)
山东理工大学科研基金重点资助项目(2004KJZ02)
关键词
交通流
短时交通流预测
投影寻踪自回归
预测模型
Traffic Flow
Short-time Traffic Flow Forecasting, Projection Pursuit Auto Regression
Forecasting Model
