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基于小波分析与隐马尔科夫模型的短时交通流预测 预览

Prediction of Short-Time Traffic Condition Based on Wavelet Analysis and the Hidden Markov Model
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摘要 鉴于当前的城市交通拥挤不堪的现状,以及现阶段道路交通流预测时间消耗过长的弊端,将小波分析引入到城市短时交通流预测过程中,结合隐马尔科夫训练,提出一种基于小波分析的隐马尔科夫训练交通流预测模型。文章以新乡市交通局公交汽车数据和出租汽车数据作为数据来源,应用小波分析和隐马尔科夫相结合的预测模型进行预测,随后将预测结果同传统的隐马尔科夫模型所预测的结果进行对比分析。实验表明,本模型预测结果精确,与真实数据更为贴近,同时有效的降低了交通流预测的时间损耗,在短时交通流预测方面更加具有优越性。 In view of the current situation of urban traffic congestion and the high cost of prediction of the traffic condition, this paper introduces the wavelet analysis into the urban short-term traffic flow forecasting process, combined with the hid- den markov training, and proposes a forecasting model. We use the data of buses and taxis from the transportation bureau of Xinxiang city as the data source and predict short-time traffic condition by using the model. Then the prediction results are analyzed. Compared with the traditional hidden markov model, the experimental results show that our model is more accurate and efficient.
作者 王川 张宝文 WANG Chuan, ZHANG Baowen (Department of Computer and Information Engineering, Henan Normal University, Xinxiang Henan 453007)
出处 《交通节能与环保》 2018年第1期43-47,共5页
基金 新乡市科技创新平台建设项目(CP1501).
关键词 智能交通系统 短时交通流预测 小波分析 隐马尔科夫模型 intelligent transportation system short-time traffic condition forecast waveletanalysis hiddenMarkovrnodel
作者简介 王川(1976-),男,河南新乡人,副教授,研究方向为计算机应用工程。
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