计算机技术与发展2016，Vol.26Issue(11)：77-81,85,6.DOI:10.3969/j.issn.1673-629X.2016.11.017
基于ARIMA和灰色模型加权组合的短期交通流预测
Short-term Traffic Flow Forecasting Based on Combination of ARIMA and Gray Model
摘要
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.关键词
数据融合/ARIMA/灰色模型/加权/小波分析Key words
data fusion/ARIMA/gray model/weighted/wavelet analysis分类
交通工程引用本文复制引用
谈苗苗,成孝刚,周凯,李海波..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):77-81,85,6.基金项目
国家自然科学基金资助项目(61401236) （61401236）
南京邮电大学引进人才项目(NY214005) （NY214005）
