计算机工程与应用2016，Vol.52Issue(12)：246-250,5.DOI:10.3778/j.issn.1002-8331.1407-0613
融合时空信息的短时交通流预测
Short-term traffic flow forecasting by fusing spatial-temporal traffic information
摘要
Abstract
In order to accurately describe the spatial-temporal evolution of the traffic flow and improve the accuracy of short-term traffic flow prediction, this paper builds the prediction model based on GM(1,N)-Markov chain, by fusing the spatial-temporal traffic information. Firstly, the prediction section and the association sections are taken as a grey system, and the grey relational analysis of the sections and setting the lowest threshold of grey correlation degree can mine the spatial information and reduce the ineffective information; secondly, the prediction section and strong correlation sections are analyzed systematically and then predicted by GM(1,N)model. In view of the failure caused by the randomness of sequence, the Markov chain is introduced to modify the forecasting model; finally, VISSIM is used for simulating, the simulation respectively taken 2 min, 5 min, 10 min as a time interval, the predicted average relative error is 9.30%, 5.95%and 3.20%respectively, the model accuracy is optimal, and this model is proven to be effective.关键词
智能交通/交通流预测/灰色系统/马尔科夫链Key words
intelligent transportation/traffic flow forecasting/grey system/Markov chain分类
交通工程引用本文复制引用
褚鹏宇,刘澜,尹俊淞,卢维科..融合时空信息的短时交通流预测[J].计算机工程与应用,2016,52(12):246-250,5.基金项目
四川省科技支撑计划项目（No.2014GZ0019）；四川省重点实验室研究基金项目（No.szjj2011-031）。 （）
