本文選題：交通流網(wǎng)絡(luò)LWR模型　切入點(diǎn)：GPS　出處：《北京交通大學(xué)》2017年碩士論文

【摘要】：城市交通一直是城市經(jīng)濟(jì)發(fā)展和人們?nèi)粘；顒?dòng)的關(guān)鍵所在,隨著經(jīng)濟(jì)發(fā)展的逐步加快,交通擁堵現(xiàn)象日趨嚴(yán)重,交通基礎(chǔ)設(shè)施建設(shè)已經(jīng)不能滿足人們的出行需求。同時(shí)城市交通狀況愈發(fā)的復(fù)雜化,因此人們希望找到一種方法從整體上反映城市交通的變化規(guī)律。宏觀交通流模型由于其反映了整個(gè)交通網(wǎng)絡(luò)狀況,對(duì)總體把控交通狀態(tài)具有指導(dǎo)性意義,所以在交通流模型中顯得尤其重要。在宏觀交通流模型中,LWR模型具有參數(shù)少、易于計(jì)算仿真等優(yōu)點(diǎn),是目前最為廣泛使用的方法。但一般情況下,LWR模型適用于結(jié)構(gòu)相對(duì)簡(jiǎn)單的交通仿真,例如連續(xù)二維平面、直線道路等等。而實(shí)際上城市路網(wǎng)結(jié)構(gòu)復(fù)雜,干擾因素多,把LWR模型與真實(shí)路網(wǎng)結(jié)合起來一直存在諸多難點(diǎn)。所以本文基于LWR模型對(duì)于實(shí)際道路的仿真在宏觀交通流研究中很有意義。本文基于交通流網(wǎng)絡(luò)LWR模型,闡述了模型結(jié)構(gòu)、對(duì)應(yīng)數(shù)值格式以及Riemann等相關(guān)問題。同時(shí)結(jié)合出租車GPS數(shù)據(jù),獲取相關(guān)交通信息。結(jié)合實(shí)際路網(wǎng)數(shù)據(jù),完成了路網(wǎng)數(shù)據(jù)提取工作,并在此基礎(chǔ)上進(jìn)行出租車軌跡數(shù)據(jù)匹配,獲得路網(wǎng)相關(guān)交通流參數(shù)。最終將交通流網(wǎng)絡(luò)LWR模型、GPS數(shù)據(jù)以及路網(wǎng)數(shù)據(jù)三者進(jìn)行融合,得到了基于路網(wǎng)交通流LWR模型的仿真結(jié)果。本文主要工作內(nèi)容包括:1.介紹了宏觀LWR模型的發(fā)展歷程,討論了交通流模型的一般形式,闡述了多種經(jīng)典模型。然后從計(jì)算的角度出發(fā),介紹了模型的基本性質(zhì)以及數(shù)值格式。并在一階LWR模型的基礎(chǔ)上說明了交通流網(wǎng)絡(luò)LWR模型的構(gòu)造方法、Riemann問題以及模型的計(jì)算方法。2.從數(shù)據(jù)的角度出發(fā),以出租車GPS數(shù)據(jù)為基礎(chǔ),進(jìn)行了數(shù)據(jù)預(yù)處理、數(shù)據(jù)挖掘,特征提取等工作;以路網(wǎng)數(shù)據(jù)為基礎(chǔ),完成了路網(wǎng)數(shù)據(jù)預(yù)處理、道路方向確定、路網(wǎng)細(xì)化等工作。最后將二者結(jié)合,完成了道路匹配。3.基于交通流網(wǎng)絡(luò)LWR模型在路網(wǎng)上進(jìn)行仿真,并根據(jù)交通流參數(shù)對(duì)模型內(nèi)部參數(shù)進(jìn)行了調(diào)整。本文的主要成果包括:1.基于北京市道路信息以及特征提取結(jié)果得到了北京市主要城市道路網(wǎng)。2.通過匹配獲取了路網(wǎng)中各個(gè)路段出租車速度、流量等交通流參數(shù)。3.完成了交通流網(wǎng)絡(luò)LWR模型在路網(wǎng)上進(jìn)行仿真。文章中共包含圖25幅,表10個(gè),參考文獻(xiàn)60篇。包含了數(shù)據(jù)處理、路網(wǎng)提取、模型仿真等各部分內(nèi)容。文章的數(shù)據(jù)處理過程對(duì)于交通流模型在實(shí)際路網(wǎng)上仿真提供了可靠依據(jù),得到的仿真結(jié)果對(duì)于LWR模型在實(shí)際城市交通網(wǎng)絡(luò)中的應(yīng)用以及宏觀交通流研究都具有一定的意義。
[Abstract]:Urban traffic has always been the key to the development of urban economy and people's daily activities. With the gradual acceleration of economic development, traffic congestion is becoming more and more serious. Transportation infrastructure has not been able to meet people's travel needs. Meanwhile, the urban transportation situation is becoming more and more complicated. Therefore, people hope to find a way to reflect the changing law of urban traffic on the whole. Because the macroscopic traffic flow model reflects the whole traffic network condition, it is of guiding significance to control the traffic state as a whole. So it is especially important in the traffic flow model. In the macroscopic traffic flow model, the LWR model has the advantages of less parameters, easy calculation and simulation, etc. It is the most widely used method at present. But in general, the LWR model is suitable for traffic simulation with relatively simple structure, such as continuous two-dimensional plane, straight road, etc. In fact, the structure of urban road network is complex, and there are many interference factors. There are many difficulties in combining the LWR model with the real road network. Therefore, the simulation based on the LWR model is of great significance in the study of the macroscopic traffic flow. Based on the LWR model of the traffic flow network, this paper expounds the structure of the model. Corresponding numerical format and Riemann and other related problems. At the same time combined with taxi GPS data to obtain relevant traffic information. Combined with the actual road network data, completed the road network data extraction work, and on the basis of the taxi track data matching, Finally, the traffic flow network LWR model and road network data are fused together. The simulation results of traffic flow LWR model based on road network are obtained. The main work of this paper includes: 1. The development history of macro LWR model is introduced, and the general form of traffic flow model is discussed. Several classical models are described. Then, from the point of view of calculation, This paper introduces the basic properties and numerical format of the model, and on the basis of the first-order LWR model, explains the construction method of the LWR model of traffic flow network and the calculation method of the model. 2. From the point of view of the data, based on the data of the taxi GPS, the paper introduces the method of constructing the LWR model of the traffic flow network and the calculation method of the model. Data preprocessing, data mining, feature extraction and so on are carried out. Based on road network data, road network data preprocessing, road direction determination, road network refinement and so on are completed. The road matching. 3. Based on the traffic flow network LWR model, the road network simulation is carried out. The main results of this paper are as follows: 1. Based on the road information and feature extraction of Beijing, the road network of main cities in Beijing is obtained. 2. The road network is obtained by matching. Taxi speed in various sections of the network, 3. The traffic flow network LWR model is simulated on the road network. The paper includes 25 figures, 10 tables, 60 references, including data processing, road network extraction, and so on. The data processing process of the paper provides a reliable basis for the traffic flow model simulation on the actual road network. The simulation results are significant for the application of LWR model in urban traffic network and the study of macroscopic traffic flow.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 梁軍輝;林堅(jiān);杜洋;;大數(shù)據(jù)條件下城市用地類型辨識(shí)研究——基于出租車GPS數(shù)據(jù)的動(dòng)態(tài)感知[J];上海國(guó)土資源;2016年01期

2 張紅;王曉明;過秀成;曹潔;朱昶勝;郭義戎;;出租車GPS軌跡大數(shù)據(jù)在智能交通中的應(yīng)用[J];蘭州理工大學(xué)學(xué)報(bào);2016年01期

3 羅振東;徐源;;守恒高階各向異性交通流模型基于POD方法的降階外推差分格式[J];應(yīng)用數(shù)學(xué)和力學(xué);2015年08期

4 胡彥梅;封建湖;陳建忠;;多車種LWR交通流模型的半離散中心迎風(fēng)格式[J];計(jì)算物理;2014年03期

5 馬健;張麗巖;李克平;孫劍;朱從坤;;一個(gè)新的基于LWR模型的中觀交通流運(yùn)動(dòng)學(xué)模型[J];武漢理工大學(xué)學(xué)報(bào)(交通科學(xué)與工程版);2013年04期

6 吳春秀;宋濤;張鵬;黃仕進(jìn);;守恒高階交通流模型的相平面分析[J];應(yīng)用數(shù)學(xué)和力學(xué);2012年12期

7 王靜波;王競(jìng)濤;;基于波動(dòng)方程的動(dòng)態(tài)交通流網(wǎng)絡(luò)模型研究[J];計(jì)算機(jī)仿真;2012年01期

8 冀峰;趙偉;李榮冰;劉建業(yè);;GPS接收機(jī)時(shí)鐘頻率漂移誤差分析及模型預(yù)測(cè)[J];數(shù)據(jù)采集與處理;2010年04期

9 黃冠利;王輝;徐華平;;基于時(shí)間序列解決GPS信號(hào)定位漂移的研究[J];計(jì)算機(jī)工程與應(yīng)用;2008年31期

10 張鵬;蔣志浩;秘金鐘;黨亞民;;我國(guó)GPS跟蹤站數(shù)據(jù)處理與時(shí)間序列特征分析[J];武漢大學(xué)學(xué)報(bào)(信息科學(xué)版);2007年03期

相關(guān)碩士學(xué)位論文 前4條

1 王芮;基于GPS數(shù)據(jù)的城市出租車出行需求研究[D];山東大學(xué);2016年

2 李汝佟;基于出租車GPS數(shù)據(jù)的城市公交線網(wǎng)優(yōu)化[D];電子科技大學(xué);2014年

3 何巍楠;基于浮動(dòng)車數(shù)據(jù)的城市常發(fā)性交通擁堵時(shí)空分布特征研究[D];北京交通大學(xué);2012年

4 童曉君;基于出租車GPS數(shù)據(jù)的居民出行行為分析[D];中南大學(xué);2012年

，

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