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交通流预测分析已成为智能交通的核心研究内容之一。依据混沌时间序列分析方法，建立了短时交通流的预测模型。在对实测的交通流数据进行相空间重构的基础上，综合考虑欧氏距离和均等系数，提出了最邻近点的两步优化选择方法,并采用了局部多项式拟合方法对所选取的最邻近点进行逼近以求得预测公式。本文将此方法运用于东莞东江大道流量预测，比较预测流量和实测流量，得出最大相对误差为0.445％，最小相对误差为0.038％，且单步预测时间仅为38.52秒。结果表明，该预测模型具有较高的精度，同时也能够满足实时性的要求。

Short-Time Traffic Flow Prediction Using Chaos Time Series Theory

Traffic flow prediction has become a key issue in intelligent transportation system study. In this paper, a prediction model of short-time traffic flow is presented based on the chaotic time series analysis. After the phase space reconstruction using traffic flow data, a two-step optimized selection method is proposed which considers Euclidean distance and equal coefficient between neighboring point and predicted point. In addition, the prediction model is developed by local polynomial method to approximate the neighboring points. The model proposed in this paper is applied to predict the real traffic flow in Dongjiang Road, Dong Guan. Comparing the traffic flow predicting value with the measure value, the results indicate that the maximal relative error is 0.445% and the minimal one is 0.038%. Moreover, single-step ahead prediction only requires 38.52 seconds. It is proved that the proposed method can significantly improve the prediction accuracy and meet the requirement of the real-time prediction.