本文選題：短期交通流預測　切入點：深度學習　出處：《南京郵電大學》2017年碩士論文　論文類型：學位論文

【摘要】：道路交通系統(tǒng)是一個國家國民經濟發(fā)展的基礎,建造一個合理高效的道路交通系統(tǒng)是至關重要的。隨著人們出行需求的增多,大眾和企業(yè)對道路交通系統(tǒng)的便捷度要求越來越高。為了解決道路擁堵的狀況,我們著力于研究交通流預測技術,期望能獲取未來短期時間內精準的車流量數據,以實現(xiàn)車輛分流、交通誘導、道路規(guī)劃和交通設施合理分布等目的。本文主要的研究對象是交通流數據,研究目標是精確的預測某選定路段未來一天以內的交通流量,研究內容是交通流數據預處理、交通流預測模型搭建和預測效果檢驗,設計了預測精度較高的交通流預測算法。本文的主要研究內容和研究成果如下:(1)本文首先對交通流數據的參數、特征和影響因素進行分析,選取時間和車流量作為本文的研究參數。使用EViews數據分析器獲取數據的季節(jié)性和趨勢性特征,為選取合適的非線性預測模型做鋪墊。然后對交通流數據進行預處理。使用SPSS數據分析器調整數據順序、添加空缺值并改正非常規(guī)值;再對數據進行小波軟閾值去噪,去噪過程包括小波分解、軟閾值去噪和小波重構,使用matlab代碼實現(xiàn)并獲得去噪后的數據表和數據圖。(2)建立交通流數據的LSTM模型和GM模型。LSTM模型使用keras框架和python代碼編寫。將預處理后的部分數據輸入進搭建好的LSTM網絡,LSTM通過學習數據的特征確定網絡參數和權值,并輸出未來一天的交通流數據。LSTM模型的預測效果較好,但是模型訓練需要的數據量較大。GM屬于灰色模型,我們采取10個數據一個模型,不斷改變模型參數,構造動態(tài)灰色模型。GM模型的預測效果不如LSTM模型的預測效果好,但是預測所需的數據量較少且實時性強。(3)將LSTM模型和GM模型使用動態(tài)權值w集成。針對單個模型預測法在應對突發(fā)狀況時容易遺漏和忽視,導致預測精度降低,采用兩種預測模型集成的方式對交通流預測進行研究。集成方式為加權組合,權值w利用關聯(lián)系數確定,權值的動態(tài)步調與GM的建模步調保持一致。集成模型的預測結果顯示,其預測精確度比兩個模型單獨預測的精確度高。
[Abstract]:The road traffic system is the foundation of a country's national economic development. It is very important to build a reasonable and efficient road traffic system. In order to solve the problem of road congestion, we are working on traffic flow forecasting technology to get accurate traffic data in the short term in the future. In order to realize the purpose of vehicle shunt, traffic guidance, road planning and reasonable distribution of traffic facilities, the main research object of this paper is traffic flow data, the research goal is to accurately predict the traffic flow of a selected section of the road within one day in the future. The content of the research is traffic flow data preprocessing, traffic flow forecasting model building and forecasting effect testing. A traffic flow forecasting algorithm with high precision is designed. The main contents and results of this paper are as follows: (1) in this paper, the parameters, characteristics and influencing factors of traffic flow data are analyzed. Time and traffic flow are selected as the parameters of this paper. The seasonal and trend characteristics of the data are obtained by using the EViews data analyzer. In order to select the suitable nonlinear prediction model, the traffic flow data is preprocessed. The SPSS data analyzer is used to adjust the data order, add the vacant value and correct the unconventional value, and then the wavelet soft threshold is used to de-noise the data. The denoising process includes wavelet decomposition, soft threshold denoising and wavelet reconstruction. The LSTM model of traffic flow data and GM model. LSTM model are written using keras framework and python code. The pre-processed part of the data is input into the constructed LSTM. The LSTM determines the network parameters and weights by learning the characteristics of the data. And output the traffic flow data. LSTM model in the next day has good prediction effect, but the model training needs a large amount of data. GM belongs to the grey model. We take 10 data and one model, and constantly change the model parameters. The prediction effect of dynamic grey model. GM model is not as good as that of LSTM model. But the amount of data needed for prediction is less and real-time. 3) the LSTM model and GM model are integrated with dynamic weight w. The prediction method of single model is easy to be omitted and ignored when dealing with sudden situation, which leads to the decrease of prediction accuracy. Traffic flow forecasting is studied by two integrated forecasting models. The integration method is a weighted combination, the weight w is determined by the correlation coefficient, the dynamic step of the weight value is consistent with the GM modeling step, and the prediction results of the integrated model show that, Its prediction accuracy is higher than that of the two models alone.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U491.14

【參考文獻】

相關期刊論文 前3條

1 孫占全;潘景山;張贊軍;張立東;丁青艷;;基于主成分分析與支持向量機結合的交通流預測[J];公路交通科技;2009年05期

2 張九躍;焦玉棟;;基于RBF神經網絡的短時交通流量預測[J];山東交通學院學報;2008年03期

3 唐鐵橋,黃海軍;用燕尾突變理論來討論交通流預測[J];數學研究;2005年01期

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本文編號：1619958