本文選題：微觀交通仿真 + 動(dòng)態(tài)數(shù)據(jù)驅(qū)動(dòng)　； 參考：《南京航空航天大學(xué)》2014年碩士論文

【摘要】：隨著智能交通系統(tǒng)(ITS)日益發(fā)展，微觀交通仿真技術(shù)作為描述復(fù)雜交通行為的有效工具，在解決交通問(wèn)題方面發(fā)揮越來(lái)越大的作用。圍繞交通仿真模型建立和系統(tǒng)開(kāi)發(fā)的研究已有許多成果，但現(xiàn)有交通仿真通�；跉v史數(shù)據(jù)，忽略系統(tǒng)運(yùn)行過(guò)程中各種突發(fā)事件，在實(shí)時(shí)動(dòng)態(tài)條件下的仿真結(jié)果準(zhǔn)確性較低。各種高新技術(shù)的發(fā)展使真實(shí)交通流數(shù)據(jù)較易獲得，從而為基于動(dòng)態(tài)實(shí)測(cè)數(shù)據(jù)的交通仿真提供了可能。動(dòng)態(tài)數(shù)據(jù)驅(qū)動(dòng)應(yīng)用系統(tǒng)(DDDAS)的特點(diǎn)是將仿真和真實(shí)數(shù)據(jù)有效結(jié)合，使仿真能夠動(dòng)態(tài)接受實(shí)測(cè)數(shù)據(jù)的反饋，從而使仿真收斂更快、結(jié)果更可信。目前，DDDAS在危機(jī)管理、工程科學(xué)與災(zāi)難預(yù)報(bào)等具有充足實(shí)測(cè)數(shù)據(jù)的領(lǐng)域得到廣泛的應(yīng)用。 基于上述背景，，本文旨在將DDDAS范式應(yīng)用于微觀仿真系統(tǒng)Movsim，提出動(dòng)態(tài)數(shù)據(jù)驅(qū)動(dòng)的交通仿真方法，該方法將車(chē)輛運(yùn)行實(shí)測(cè)數(shù)據(jù)反饋到交通狀態(tài)預(yù)測(cè)中，使預(yù)測(cè)結(jié)果更準(zhǔn)確可靠。首先，對(duì)DDDAS范式和Movsim邏輯流程、車(chē)輛模型和路網(wǎng)模型進(jìn)行分析，在此基礎(chǔ)上提出動(dòng)態(tài)數(shù)據(jù)驅(qū)動(dòng)的交通仿真框架，剖析框架運(yùn)行機(jī)制，探討并行處理、數(shù)據(jù)同化等關(guān)鍵技術(shù)。其次，構(gòu)建基于粒子濾波的交通仿真模型。粒子濾波算法給非線(xiàn)性、非高斯系統(tǒng)的狀態(tài)估計(jì)提供了嚴(yán)謹(jǐn)解決方法，因此，引入粒子濾波算法實(shí)現(xiàn)框架的數(shù)據(jù)同化部分。本文設(shè)計(jì)了隨機(jī)平移和分段車(chē)輛密度兩種噪聲模型、基于JSON中間件的數(shù)據(jù)映射模型、基于滑動(dòng)窗口和傳感器智能選擇兩種權(quán)重計(jì)算模型以及層次重采樣模型，并給出算法圖形化解釋?zhuān)榻B基于粒子濾波交通仿真模型實(shí)現(xiàn)流程。進(jìn)而，設(shè)計(jì)并實(shí)現(xiàn)了基于粒子濾波的交通仿真系統(tǒng)。結(jié)合研究建立交通仿真模型對(duì)系統(tǒng)進(jìn)行模塊劃分，對(duì)數(shù)據(jù)同化、動(dòng)態(tài)數(shù)據(jù)注入、多線(xiàn)程管理、人機(jī)交互等主要模塊進(jìn)行設(shè)計(jì)與實(shí)現(xiàn)。最后，應(yīng)用基于粒子濾波的交通仿真系統(tǒng)，構(gòu)建直線(xiàn)和環(huán)形道路的仿真場(chǎng)景，對(duì)動(dòng)態(tài)數(shù)據(jù)驅(qū)動(dòng)的交通仿真框架的應(yīng)用效果和仿真精度進(jìn)行實(shí)驗(yàn)和分析。
[Abstract]:With the development of Intelligent Transportation system (its), micro-traffic simulation technology, as an effective tool to describe complex traffic behaviors, plays an increasingly important role in solving traffic problems. Many achievements have been made on the establishment of traffic simulation model and the development of traffic simulation system. However, the existing traffic simulation is usually based on historical data, neglecting all kinds of unexpected events in the course of system operation, and the accuracy of simulation results under real-time and dynamic conditions is low. With the development of high and new technology, the real traffic flow data can be easily obtained, which makes it possible for traffic simulation based on dynamic measured data. Dynamic data driven application system (DDDAS) is characterized by the effective combination of simulation and real data, so that the simulation can dynamically accept the feedback of measured data, so that the simulation converges faster and the results are more reliable. At present, DDDAS is widely used in the field of crisis management, engineering science and disaster prediction. Based on the above background, this paper aims to apply DDDAS paradigm to the microscopic simulation system Movsimand and propose a dynamic data-driven traffic simulation method. The method feedbacks the measured data of vehicle operation into the traffic state prediction to make the prediction results more accurate and reliable. Firstly, the DDDAS paradigm, Movsim logical flow, vehicle model and road network model are analyzed. Based on this, a dynamic data-driven traffic simulation framework is proposed. The running mechanism of the framework is analyzed, and the key technologies such as parallel processing and data assimilation are discussed. Secondly, the traffic simulation model based on particle filter is constructed. Particle filter algorithm provides a rigorous solution to the state estimation of nonlinear, non-Gao Si systems. Therefore, the particle filter algorithm is introduced to implement the data assimilation part of the framework. In this paper, two kinds of noise models, random translation model and segmented vehicle density model, data mapping model based on JSON middleware, two weight calculation models based on sliding window and sensor intelligent selection, and hierarchical resampling model are designed. A graphical explanation of the algorithm is given, and the flow chart of traffic simulation model based on particle filter is introduced. Furthermore, the traffic simulation system based on particle filter is designed and implemented. Combined with the research and establishment of traffic simulation model, the system is divided into modules, and the main modules such as data assimilation, dynamic data injection, multi-thread management and human-computer interaction are designed and implemented. Finally, the traffic simulation system based on particle filter is used to construct the simulation scene of straight line and ring road, and the application effect and simulation precision of the dynamic data driven traffic simulation framework are tested and analyzed.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：U495

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 毛鋒;李強(qiáng);;城市公共安全的應(yīng)急響應(yīng)模擬[J];城市問(wèn)題;2010年07期

2 王秋平;周原;康順;左玲;;改進(jìn)權(quán)值計(jì)算的EPF算法及在目標(biāo)跟蹤中的應(yīng)用[J];電光與控制;2011年04期

3 馬建文,田國(guó)良,王長(zhǎng)耀,燕守勛;遙感變化檢測(cè)技術(shù)發(fā)展綜述[J];地球科學(xué)進(jìn)展;2004年02期

4 金偉正;金湯;;嵌入式操作系統(tǒng)Visual DSP++Kernel的原理與應(yīng)用[J];電氣電子教學(xué)學(xué)報(bào);2011年02期

5 鄒智軍,楊東援;道路交通仿真研究綜述[J];交通運(yùn)輸工程學(xué)報(bào);2001年02期

6 員天佑;劉金;李潮;;基于物聯(lián)網(wǎng)與DDDAS的橋梁監(jiān)測(cè)與損傷評(píng)估系統(tǒng)設(shè)計(jì)[J];計(jì)算機(jī)測(cè)量與控制;2013年03期

7 陳海燕;王建東;徐濤;;動(dòng)態(tài)數(shù)據(jù)驅(qū)動(dòng)的航班延誤預(yù)測(cè)研究[J];武漢理工大學(xué)學(xué)報(bào)(交通科學(xué)與工程版);2012年03期

8 鄧可;;基于動(dòng)態(tài)數(shù)據(jù)驅(qū)動(dòng)的半導(dǎo)體生產(chǎn)線(xiàn)調(diào)度仿真[J];上海管理科學(xué);2011年03期

9 鄒智軍,楊東援;城市交通仿真實(shí)驗(yàn)系統(tǒng)的面向?qū)ο箝_(kāi)發(fā)[J];系統(tǒng)仿真學(xué)報(bào);2002年07期

10 馮馳;王萌;汲清波;;粒子濾波器重采樣算法的分析與比較[J];系統(tǒng)仿真學(xué)報(bào);2009年04期

相關(guān)博士學(xué)位論文 前1條

1 周云;面向?qū)崟r(shí)作戰(zhàn)決策支持的動(dòng)態(tài)數(shù)據(jù)驅(qū)動(dòng)仿真理論和方法研究[D];國(guó)防科學(xué)技術(shù)大學(xué);2010年

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