[1]谈苗苗,成孝刚,周凯,等. 基于ARIMA和灰色模型加权组合的短期交通流预测[J].计算机技术与发展,2016,26(11):77-81.
　TAN Miao-miao,CHENG Xiao-gang,ZHOU Kai,et al. Short-term Traffic Flow Forecasting Based on Combination of ARIMA and Gray Model[J].,2016,26(11):77-81.
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 基于ARIMA和灰色模型加权组合的短期交通流预测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年11期
页码:
77-81
栏目:
智能、算法、系统工程
出版日期:
2016-11-10

文章信息/Info

Title:
 Short-term Traffic Flow Forecasting Based on Combination of ARIMA and Gray Model
文章编号:
1673-629X（2016)11-0077-05
作者:
 谈苗苗; 成孝刚; 周凯; 李海波
 南京邮电大学 通信与信息工程学院
Author(s):
 TAN Miao-miao; CHENG Xiao-gang; ZHOU Kai; LI Hai-bo
关键词:
 ; 数据融合; ARIMA ; 灰色模型; 加权; 小波分析
Keywords:
 ; data fusion; ARIMA ; gray model; weighted ; wavelet analysis
分类号:
U491.112
文献标志码:
A
摘要:
 交通流预测是智能交通系统至关重要的一部分，应用于交通流预测的方法非常多，由于实际路况的复杂性和单个方法的局限性，现有方法的精确度亟待提高。为解决这一问题，采用数据融合的方法，对传感器采集的原始数据做数据预处理，利用小波分析去除信号多余的噪声，然后利用ARIMA模型和灰色模型分别对同一交通流序列进行建模，得出两者各自的预测结果后，找出最佳权值对两种模型的结果进行加权，得到数据融合后的预测结果。仿真结果表明，该组合模型改善了单个预测方法的短处，使得预测精度有所提高。
Abstract:
 Traffic flow forecasting is a very important part of the intelligent transportation system. There are many methods for traffic flow forecasting,most of them have good results for the traffic flow. However,due to the limitations of a single forecasting method,it cannot guarantee the accuracy of prediction in different situations. In order to solve this problem,the method of data fusion is used. The original data by sensors is carried out in data preprocessing,using wavelet analysis to remove the excess noise. Then,the ARIMA model and gray model are used to model the same traffic flow series. After the results of the two projections are come out,getting the optimal weights,and the results of the two models are weighted,and the results are obtained after data fusion. The simulation results show that the combination model improves the shortcomings of the single forecasting method,which makes the prediction accuracy improved.

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更新日期/Last Update: 2016-12-09