本文選題：復(fù)合網(wǎng)絡(luò) + 復(fù)雜網(wǎng)絡(luò)　； 參考：《西南大學(xué)》2017年碩士論文

【摘要】：在現(xiàn)代社會(huì)中公共交通系統(tǒng)一直充當(dāng)著重要的角色,無(wú)論在實(shí)踐中還是學(xué)術(shù)中,交通問(wèn)題極大地引起了各界的注意。伴隨著交通工具的不斷普及,乘客出行量逐年增加,公交與地鐵換乘不協(xié)調(diào)、交通擁擠及城市公共交通系統(tǒng)整體運(yùn)營(yíng)效率低等問(wèn)題日益凸顯。如何提升城市公共交通系統(tǒng)的運(yùn)輸效率,已成為交通領(lǐng)域的熱點(diǎn)話題。在如今大數(shù)據(jù)的時(shí)代背景下,通過(guò)對(duì)海量、多樣化的交通數(shù)據(jù)進(jìn)行挖掘,不僅能夠分析出公共交通網(wǎng)絡(luò)的拓?fù)涮卣?同時(shí)也能挖掘出乘客的出行行為規(guī)律,對(duì)提升公交與地鐵之間的高效配合以及公共交通的綜合運(yùn)輸能力具有重要的現(xiàn)實(shí)意義。首先,本文通過(guò)構(gòu)建公交-地鐵復(fù)合網(wǎng)絡(luò),并分析了復(fù)合交通網(wǎng)絡(luò)的拓?fù)湫再|(zhì)及魯棒性;其次,利用基于多頭絨泡菌仿生模型改進(jìn)的粒子群算法,對(duì)地鐵乘客出行網(wǎng)絡(luò)進(jìn)行社團(tuán)劃分;最后,采用非負(fù)矩陣分解-自回歸模型,對(duì)地鐵乘客動(dòng)態(tài)起訖(Origin Destination,OD)矩陣進(jìn)行預(yù)測(cè)。本文的主要貢獻(xiàn)如下:(1)實(shí)現(xiàn)構(gòu)建公交-地鐵復(fù)合網(wǎng)絡(luò),同時(shí)對(duì)比分析復(fù)合網(wǎng)絡(luò)與子網(wǎng)絡(luò)的拓?fù)涮匦约棒敯粜?通過(guò)采用兩種建模方式(Space L方式、Space P方式)構(gòu)建了公交-地鐵復(fù)合站點(diǎn)網(wǎng)絡(luò)及公交-地鐵復(fù)合換乘網(wǎng)絡(luò),并對(duì)比分析兩種復(fù)合網(wǎng)絡(luò)與其相應(yīng)模式下公交子網(wǎng)絡(luò)與地鐵子網(wǎng)絡(luò)的拓?fù)涮卣髦�。此�?對(duì)比分析復(fù)合網(wǎng)絡(luò)與子網(wǎng)絡(luò)在不同攻擊模式下的魯棒性指標(biāo),即最大連通子圖相對(duì)大小、平均路徑長(zhǎng)度、網(wǎng)絡(luò)直徑、網(wǎng)絡(luò)性能參數(shù)等魯棒性指標(biāo)的變化情況。并以中國(guó)西部某市的公交網(wǎng)絡(luò)及地鐵網(wǎng)絡(luò)數(shù)據(jù)進(jìn)行實(shí)證分析,結(jié)果表明:該市公交-地鐵網(wǎng)絡(luò)復(fù)合網(wǎng)絡(luò)、公交子網(wǎng)絡(luò)、地鐵子網(wǎng)絡(luò)都是小世界網(wǎng)絡(luò),且具有無(wú)標(biāo)度特性。復(fù)合網(wǎng)絡(luò)在隨機(jī)攻擊模式下的魯棒性較強(qiáng),然而在目標(biāo)攻擊模式下較弱;對(duì)于這兩種攻擊模式,公交-地鐵復(fù)合站點(diǎn)網(wǎng)絡(luò)的魯棒性均優(yōu)于公交子網(wǎng)絡(luò)和地鐵子網(wǎng)絡(luò)。(2)實(shí)現(xiàn)對(duì)地鐵乘客出行網(wǎng)絡(luò)的社團(tuán)劃分:通過(guò)引入多頭絨泡菌模型得到目標(biāo)函數(shù)即網(wǎng)絡(luò)模塊度的粗略解,并以此作為初始解,結(jié)合粒子群算法對(duì)模塊度函數(shù)進(jìn)行優(yōu)化求解,從而完成對(duì)乘客出行網(wǎng)絡(luò)的社團(tuán)劃分。以中國(guó)西部某市交通IC卡信息為基礎(chǔ),構(gòu)建地鐵乘客出行網(wǎng)絡(luò),并同粒子群算法進(jìn)行對(duì)比,實(shí)驗(yàn)結(jié)果表明:在對(duì)加權(quán)網(wǎng)絡(luò)進(jìn)行社團(tuán)挖掘時(shí),基于多頭絨泡菌網(wǎng)絡(luò)模型改進(jìn)的粒子群算法在解的可行性方面有了明顯提升。(3)實(shí)現(xiàn)基于非負(fù)矩陣分解-自回歸的算法來(lái)對(duì)地鐵乘客動(dòng)態(tài)OD矩陣進(jìn)行預(yù)測(cè):首先,通過(guò)非負(fù)矩陣分解得到地鐵乘客的出行特征量,然后基于非負(fù)矩陣分解得到系數(shù)矩陣建立自回歸模型,從而完成對(duì)地鐵乘客出行流量的預(yù)測(cè)。并以中國(guó)西部某市的地鐵乘客流量數(shù)據(jù)為基礎(chǔ),通過(guò)與K近鄰、C4.5、樸素貝葉斯、隨機(jī)森林等回歸算法進(jìn)行對(duì)比,實(shí)驗(yàn)結(jié)果表明,該算法的預(yù)測(cè)準(zhǔn)確率有顯著提升。
[Abstract]:In modern society, the public transportation system has been playing an important role, whether in practice or academic, traffic problems have attracted great attention from all walks of life. With the continuous popularization of transportation, passenger travel volume increases year by year, bus and subway transfer is not coordinated, traffic congestion and the urban public transport system as a whole low efficiency and other problems become increasingly prominent. How to improve the transport efficiency of urban public transport system has become a hot topic in the field of transportation. Under the background of big data's times, through mining massive and diversified traffic data, not only can the topological characteristics of public transport network be analyzed, but also the travel behavior rules of passengers can be mined. It is of great practical significance to improve the efficient cooperation between public transportation and subway and the comprehensive transportation capacity of public transportation. First of all, this paper constructs the bus-subway complex network, and analyzes the topological properties and robustness of the complex traffic network. Secondly, using the improved particle swarm optimization algorithm based on the bionic model of polycephalus, Finally, the non-negative matrix factorization-autoregressive model is used to predict the dynamic origin derivation of subway passengers. The main contributions of this paper are as follows: (1) realizing the construction of a bus-subway composite network. At the same time, the topological characteristics and robustness of the composite network and the sub-network are compared and analyzed. By using two modeling methods, the bus-subway compound station network and the bus-subway complex transfer network are constructed by adopting two modeling methods: the space L mode and the space P mode. The topological eigenvalues of two kinds of complex networks and their corresponding modes are compared and analyzed. In addition, the robustness indexes of composite network and subnetwork under different attack modes are compared and analyzed, such as the relative size of maximum connected subgraph, average path length, network diameter, network performance parameters, and so on. Based on the data of public transport network and subway network in a certain city in western China, the results show that the bus subway network, bus subnetwork and subway subnetwork are small world networks, and have scale-free characteristics. The robustness of compound network in random attack mode is stronger than that in target attack mode. The robustness of the bus-subway complex station network is better than that of the bus sub-network and the subway sub-network. It realizes the community division of the subway passenger travel network. The objective function, that is, the rough solution of the network modularity, is obtained through the introduction of the multi-headed bacterial model. As the initial solution, the modular degree function is solved optimally with particle swarm optimization (PSO), and the community partition of passenger travel network is completed. Based on the IC card information of a certain city in western China, the subway passenger travel network is constructed and compared with the particle swarm optimization algorithm. The experimental results show that: when mining the weighted network, The improved particle swarm optimization algorithm based on the multi-headed Particle Swarm Optimization (PSO) model has significantly improved the feasibility of the solution. (3) based on the non-negative matrix factorization and autoregressive algorithm to predict the dynamic OD matrix of subway passengers: first of all, The travel characteristic quantity of subway passengers is obtained by non-negative matrix decomposition, and then the autoregressive model is established based on non-negative matrix decomposition to predict the travel flow of subway passengers. Based on the data of subway passenger flow in a certain city in western China, the prediction accuracy of this algorithm is obviously improved by comparing it with K-nearest neighbor C4.5, naive Bayes, random forest and other regression algorithms.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U12;O157.5

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本文編號(hào)：1819101