非参数回归算法在短期交通流预测中的应用

Wang Xinying, J. Zhicai, Liu Miao, Sun Yuan
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引用次数: 4

摘要

短期交通流预测是智能交通系统研究领域的一个重要课题。本文分析了短期交通流预测的初步结果,充分利用k -最近邻(KNN)分类器的特点,建立了基于非参数回归算法的模型。利用KNN分类器对历史数据和测量数据进行分类,并利用KNN分类器的输出构造状态向量。下一时段的交通流预测完全基于状态向量。实验结果表明,该模型精度较高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Application of Nonparametric Regressive Algorithm for Short-Term Traffic Flow Forecast
Short-term traffic flow forecast is an important topic in the research field of intelligent transportation systems. The article analyses the preliminary results in the short-term traffic flow forecast, takes full advantage of the characteristics of K-neatest neighbor (KNN) classifiers, and builds a model based on nonparametric regressive algorithm.The historical and metrical data is classified by KNN,and the state vector is constructed by utilizing the output of KNN classifier. Traffic flow forecasting for the next period is entirely based on the state vectors.The experimental results show that the model was verified more accurate.
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