本文選題：道路網(wǎng)絡(luò) + 復(fù)雜網(wǎng)絡(luò)��； 參考：《華中科技大學(xué)》2015年碩士論文

【摘要】：近年來(lái),交通事故,自然災(zāi)害以及人為因素等造成城市交通大面積堵塞甚至癱瘓,特別是網(wǎng)絡(luò)中少數(shù)某些關(guān)鍵單元失效會(huì)引發(fā)其他單元失效,最終導(dǎo)致道路網(wǎng)絡(luò)崩潰從而造成重大的經(jīng)濟(jì)損失,這逐漸引起眾多學(xué)者對(duì)城市道路網(wǎng)絡(luò)脆弱性的研究。隨著復(fù)雜網(wǎng)絡(luò)的研究不斷深化滲入各個(gè)學(xué)科,小世界網(wǎng)絡(luò)特性和無(wú)標(biāo)度特性作為復(fù)雜網(wǎng)絡(luò)兩大重要特性,極大的促進(jìn)了復(fù)雜網(wǎng)絡(luò)的推廣及應(yīng)用,也為道路網(wǎng)絡(luò)復(fù)雜性的探討提供了嶄新的視角。目前,關(guān)于城市道路網(wǎng)絡(luò)脆弱性研究重點(diǎn)分布于影響源的識(shí)別、評(píng)估指標(biāo)選取和評(píng)估模型的構(gòu)建等,而本文在介紹道路網(wǎng)絡(luò)脆弱性及其影響因素的基礎(chǔ)上基于級(jí)聯(lián)失效研究了不同影響源對(duì)城市道路網(wǎng)絡(luò)脆弱性的影響,尋求最具魯棒性的道路網(wǎng)絡(luò),具體內(nèi)容如下:1、城市交通網(wǎng)絡(luò)作為一個(gè)復(fù)雜巨系統(tǒng),由居民出行網(wǎng)絡(luò)和道路網(wǎng)絡(luò)組成,論文根據(jù)交通網(wǎng)絡(luò)特性并結(jié)合負(fù)載-容量模型構(gòu)建了城市雙層交通網(wǎng)絡(luò)級(jí)聯(lián)失效模型來(lái)仿真不同影響源對(duì)道路網(wǎng)絡(luò)脆弱性的影響。2、分析道路網(wǎng)絡(luò)動(dòng)態(tài)脆弱性及其指標(biāo),通過數(shù)值仿真將是否考慮級(jí)聯(lián)失效這一因素對(duì)城市道路網(wǎng)絡(luò)脆弱性指標(biāo)對(duì)比分析,結(jié)果表明考慮級(jí)聯(lián)失效下路網(wǎng)脆弱性能體現(xiàn)道路網(wǎng)絡(luò)的動(dòng)態(tài)關(guān)聯(lián)性。3、基于Python開發(fā)平臺(tái)及其內(nèi)置的Networkx復(fù)雜網(wǎng)絡(luò)包采用數(shù)值分析方法從狀態(tài)和結(jié)構(gòu)兩方面出發(fā),根據(jù)構(gòu)建的城市雙層城市道路網(wǎng)絡(luò)級(jí)聯(lián)失效模型主要探討居民出行網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)、交通需求總量、道路網(wǎng)絡(luò)容量、道路網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)以及攻擊方式五種因素對(duì)城市道路網(wǎng)絡(luò)脆弱性的影響并提出相應(yīng)控制措施。
[Abstract]:In recent years, traffic accidents, natural disasters and human factors have caused a large area of traffic congestion or even paralysis, especially the failure of a few key elements in the network will lead to the failure of other units. Finally, it leads to the collapse of the road network, resulting in significant economic losses, which has gradually caused many scholars to study the vulnerability of urban road network. With the development of complex network, the characteristics of small-world network and scale-free network, as two important characteristics of complex network, have greatly promoted the popularization and application of complex network. It also provides a new angle of view for the discussion of the complexity of road network. At present, the research on the vulnerability of urban road network is focused on the identification of the sources of influence, the selection of evaluation indicators and the construction of evaluation models, etc. On the basis of introducing the vulnerability of road network and its influencing factors, this paper studies the influence of different influence sources on the vulnerability of urban road network based on cascade failure, and seeks the most robust road network. The specific contents are as follows: 1. As a complex and huge system, the urban transportation network is composed of the resident travel network and the road network. According to the characteristics of traffic network and combined with load-capacity model, this paper constructs a cascade failure model of urban double-layer traffic network to simulate the impact of different impact sources on the vulnerability of road network, and analyzes the dynamic vulnerability of road network and its indicators. Through numerical simulation, the vulnerability index of urban road network will be compared and analyzed whether the cascading failure is considered or not. The results show that the vulnerability of road network under cascaded failure can reflect the dynamic correlation of road network. Based on the Python development platform and its built-in Networkx complex network packet, the numerical analysis method is used to analyze the state and structure of the network. According to the cascade failure model of urban double-layer urban road network, the topology of resident travel network, the total traffic demand and the capacity of road network are discussed. The influence of five factors of road network topology and attack mode on the vulnerability of urban road network is discussed and the corresponding control measures are put forward.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491

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