一种基于非参数回归的交通速度预测方法  

Traffic Speed Forecasting Method Based on Nonparametric Regression

作　　者：史殿习[1] 丁涛杰[1] 丁博[1] 刘惠[1] 

机构地区：[1]国防科学技术大学计算机学院,长沙410073

出　　处：《计算机科学》2016年第2期224-229,共6页Computer Science

基　　金：国家自然科学基金(91118008)资助

摘　　要：非参数回归模型是近年来提出的一种交通状态预测模型。为进一步提高预测精度,基于非参数回归模型的特点,针对近邻状态的选取问题,提出了基于速度变化趋势和密集度的变K近邻精确搜索策略,对原有模型的近邻匹配方式进行了改进和优化,进而提出了一种短时交通平均速度预测模型。利用北京市浮动车系统数据对算法精度进行了验证,结果表明,该模型的预测精度优于基础的非参数回归和BP神经网络模型,并能为短时交通速度预测提供可行的结果。Non-parametric regression model is a traffic forecasting model proposed in recent years.Based on the characteristics of the model,in order to improve the forecasting precision on the issue of neighboring states selection,the original neighbor matching was optimized by the classification of the trend of speed and varying K neighbors precise search strategy based on intensity,and then a short-term traffic speed forecasting model was proposed.Floating car data in Beijing was used in the experiments.Results show that the optimized model is better than normal non-parametric regression model and BP neural network model,and can provide practical speed for short-term traffic prediction.

关 键 词：非参数回归 速度预测 短时交通状态 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]