本文關鍵詞： 短時交通流預測 動態(tài)張量填充 多模式分析 矩陣分解　出處：《北京理工大學》2015年碩士論文　論文類型：學位論文

【摘要】：智能交通系統(tǒng)ITS（Intelligent Transportation Systems）是近年來發(fā)展起來的交通控制管理信息系統(tǒng)。作為智能交通系統(tǒng)重要組成部分的交通控制系統(tǒng)，交通管理系統(tǒng)以及交通誘導系統(tǒng)都要求為其提供準確的實時交通信息，而在實時信息基礎上的短時交通流預測則是實時控制和誘導的前提，是智能交通系統(tǒng)的重要理論基礎。交通流數據中蘊含著豐富的多模式特征，如何在統(tǒng)一框架下充分利用交通流數據的多模式特性仍然是一個難點問題，鑒于此，本文針對短時交通流預測問題，利用張量多線性模型在多模式上動態(tài)地對交通流數據進行分析，，構建動態(tài)交通張量模型，研究動態(tài)張量模型框架下的短時交通流預測方法，本文主要內容具體包括以下幾個方面： 第一，從多模式信息挖掘角度出發(fā)，分別構建交通流數據靜態(tài)張量模型以及動態(tài)張量模型，在張量框架下利用多線性分析對交通流數據進行分析，揭示交通流數據的多模式低秩特征，提出以交通流數據的多低秩性為基礎的短時交通流預測問題。 第二，在交通數據動態(tài)張量模型基礎以及多模式分析基礎上，提出基于矩陣分解的張量填充方法解決短時交通流預測問題，并由此提出一種以多模式矩陣分解為基礎的張量填充方法及其理論。并從多個方面測試基于矩陣分解的張量填充方法的可行性。 最后，結合動態(tài)張量填充方法與交通流動態(tài)張量模型，提出基于動態(tài)張量模型的短時交通流預測方法，并與現有的預測方法進行對比實驗，分別在正常數據下和丟失數據下對算法進行檢驗測試，實驗結果表明，在一定條件下本文提出的方法能夠準確地預測交通流量且能在統(tǒng)一框架下準確地填充丟失交通數據以及預測未來交通流量。
[Abstract]:Intelligent transportation system ITS (Intelligent Transportation Systems) is a traffic control information management system developed in recent years. As an important part of the intelligent traffic control system, traffic system, traffic management system, traffic guidance systems are required to provide accurate information for the real-time traffic, and traffic information in real time based on flow prediction is the real-time control and guidance of the premise, is an important theoretical basis for the intelligent traffic system. The traffic flow data contained in the multi mode feature rich, how to make full use of the multi mode characteristics of traffic flow data in a unified framework is still a difficult problem, in view of this, based on the short-term traffic flow prediction problem, using multi linear tensor in the multi model dynamic model of traffic flow data analysis, constructs a dynamic traffic tensor model, study on dynamic tensor model under the framework of the Short time traffic flow forecasting method, the main contents of this paper include the following aspects:
First, starting from the perspective of multimodal information mining, constructs the traffic flow data of static model and dynamic model of tensor tensor in the tensor framework, using linear analysis to analyze the traffic flow data reveal multi mode low rank characteristic data of traffic flow, the low rank of the traffic flow data in short-term traffic based flow prediction problem.
Second, based on the dynamic traffic data tensor model and the multi mode on the basis of the analysis, put forward the method to solve the tensor matrix decomposition with short term traffic flow forecasting based on the problems and put forward a kind of tensor based on multi pattern matrix decomposition based filling method and theory. And from the aspects of feasibility test based on tensor matrix decomposition filling method.
Finally, combined with the dynamic tensor filling method and dynamic traffic flow tensor model, proposed a prediction method of short term traffic flow based on dynamic tensor model, and is compared with the existing prediction methods, missing data under test to test the algorithm respectively in normal data and experimental results show that under certain conditions, this can accurate prediction of traffic flow and can accurately fill in a unified framework of lost traffic data and to predict the future traffic.

【學位授予單位】：北京理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.14

【參考文獻】

相關博士學位論文 前1條

1 李星毅;基于相似性的交通流分析方法[D];北京交通大學;2010年

本文編號：1492262