本文選題：城市道路交通網(wǎng)絡(luò)　切入點(diǎn)：空間相關(guān)性　出處：《北京交通大學(xué)》2017年博士論文　論文類型：學(xué)位論文

【摘要】：近年來,機(jī)動車、駕駛?cè)藬?shù)量及道路交通流量保持著迅猛增長的趨勢,給人們生產(chǎn)生活帶來便捷的同時(shí),也帶來不容忽視的安全隱患。分析人、車、路、環(huán)境等多方面因素對交通事故風(fēng)險(xiǎn)的影響和作用機(jī)理,構(gòu)建高效的交通事故預(yù)警模型,制定行之有效的城市道路交通主動安全保障措施,為居民的出行保駕護(hù)航已成為亟待解決的問題。交通流因素是影響城市道路交通事故風(fēng)險(xiǎn)的關(guān)鍵因素之一,利用交通流數(shù)據(jù),研究交通流動態(tài)特征對交通事故風(fēng)險(xiǎn)的影響,是對城市道路交通事故進(jìn)行精準(zhǔn)研判和提高整個(gè)城市道路交通系統(tǒng)安全水平的主要途徑。此外,面向城市路網(wǎng)深入分析和掌握交通流變化規(guī)律,提高交通流預(yù)測的實(shí)時(shí)性、可靠性和自適應(yīng)性是目前關(guān)注和研究的熱點(diǎn),為交通事故預(yù)警提供重要的數(shù)據(jù)保障。本文按交通流特性分析—交通流預(yù)測—交通事故預(yù)警的邏輯層次展開研究,重點(diǎn)解決如何分析城市路網(wǎng)交通流空間相關(guān)特性、如何利用交通流空間互相關(guān)特性實(shí)現(xiàn)城市路網(wǎng)多斷面交通流短時(shí)預(yù)測以及如何利用交通流因素進(jìn)行交通事故預(yù)警等問題。本文針對這些主要問題進(jìn)行了深入研究,并通過場景實(shí)例或數(shù)值實(shí)例對相應(yīng)的理論方法進(jìn)行了驗(yàn)證,主要研究成果具體體現(xiàn)在以下幾個(gè)方面:(1)基于復(fù)雜網(wǎng)絡(luò)理論提出了一種體現(xiàn)路段交通流空間互相關(guān)性的城市道路交通網(wǎng)絡(luò)建模和分析方法。利用復(fù)雜網(wǎng)絡(luò)理論對城市道路交通系統(tǒng)進(jìn)行建模,用以體現(xiàn)路網(wǎng)中路段交通流互相關(guān)分布的復(fù)雜性。將城市道路交通系統(tǒng)表示為一個(gè)由系統(tǒng)內(nèi)路段上的交通流空間相互關(guān)聯(lián)而生成的網(wǎng)絡(luò)。其中,網(wǎng)絡(luò)的節(jié)點(diǎn)代表路網(wǎng)中路段,節(jié)點(diǎn)之間的邊是否存在取決于節(jié)點(diǎn)對上交通流序列空間相關(guān)的程度。針對構(gòu)建的城市道路交通網(wǎng)絡(luò),提出一種考慮地理權(quán)重的PageRank算法(GWPA),用于確定路網(wǎng)中路段的重要度,為交通流相關(guān)性空間聚類的劃分提供支持。(2)從復(fù)雜網(wǎng)絡(luò)社區(qū)發(fā)現(xiàn)角度探索路網(wǎng)中路段交通流空間相關(guān)性分布規(guī)律,提出了一種基于GWPA-k-means的城市路網(wǎng)交通流空間相關(guān)性分析方法。該方法從兩方面對傳統(tǒng)的k-means算法進(jìn)行改進(jìn):針對初始種群選擇問題,提出了一種改進(jìn)的基于密度峰值的聚類中心選取算法,利用節(jié)點(diǎn)的GWPA值和最短路徑長度確定初始種群;針對節(jié)點(diǎn)相似度矩陣構(gòu)造問題,基于節(jié)點(diǎn)GWPA值,提出了一種加權(quán)的信號傳遞方法,用于測量網(wǎng)絡(luò)中節(jié)點(diǎn)的相似性,為k-means聚類提供依據(jù)。實(shí)驗(yàn)結(jié)果表明,GWPA-k-means方法能較好地揭示城市路網(wǎng)中路段交通流空間互相關(guān)模式。(3)基于交通流空間相關(guān)特性,提出了一種基于社區(qū)發(fā)現(xiàn)和長短期記憶神經(jīng)網(wǎng)絡(luò)的多斷面交通流短時(shí)預(yù)測方法。方法首先根據(jù)GWPA-k-means算法劃分的路段交通流空間互相關(guān)模式將路網(wǎng)劃分為若干個(gè)區(qū)域,位于同一個(gè)區(qū)域內(nèi)的路段之間交通流空間相關(guān)性較強(qiáng);然后針對每個(gè)區(qū)域路網(wǎng),以路段交通流序列構(gòu)成的二維矩陣序列為輸入,利用長短期記憶神經(jīng)網(wǎng)絡(luò)對選取交通流數(shù)據(jù)進(jìn)行時(shí)空特征學(xué)習(xí),進(jìn)而實(shí)現(xiàn)多斷面交通流短時(shí)預(yù)測。針對預(yù)測模型參數(shù)設(shè)置問題,提出了一種基于自適應(yīng)正交遺傳算法的模型參數(shù)優(yōu)選算法。實(shí)驗(yàn)結(jié)果表明,考慮城市路網(wǎng)交通流空間相關(guān)性可以提升交通流預(yù)測精度,該方法在傳感器故障造成數(shù)據(jù)缺失情況下適應(yīng)性良好,具有較強(qiáng)的魯棒性。(4)以交通流數(shù)據(jù)為基礎(chǔ),提出了基于交通流因素的城市道路交通事故預(yù)警方法。在實(shí)時(shí)獲取交通流狀態(tài)的基礎(chǔ)上,分析交通流因素對交通事故風(fēng)險(xiǎn)的影響,并分別從交通事故檢測和交通事故風(fēng)險(xiǎn)預(yù)測兩方面對交通事故預(yù)警方法進(jìn)行研究。以一種域劃分的角度去闡明交通流變量與交通事故風(fēng)險(xiǎn)的關(guān)系,給出了交通安全域的概念;提出了一種基于序列向前選擇和主成分分析的特征提取算法,用于提取影響交通事故風(fēng)險(xiǎn)的顯著特征變量;提出了一種基于最小二乘支持向量機(jī)的安全域估計(jì)方法,用交通安全域?qū)崟r(shí)檢測交通事故。此外,基于實(shí)時(shí)獲取的交通流數(shù)據(jù),將交通安全域和可靠性分析理論相結(jié)合,提出了一種交通可靠性模型,該模型可同時(shí)對交通事故風(fēng)險(xiǎn)進(jìn)行宏觀的統(tǒng)計(jì)評價(jià)和面向單個(gè)交通事故的實(shí)時(shí)風(fēng)險(xiǎn)預(yù)測。
[Abstract]:In recent years, the number of motor vehicles, and road traffic driver to maintain rapid growth trend, bring convenience to people's production and life, but also brings security risks can not be ignored. Analysis of people, vehicles, roads, and many factors affecting the environment influence on traffic accident risk mechanism, construction of the road traffic accident, early warning model the development of effective active city road traffic security measures for the residents travel escort has become a serious problem. The traffic flow is a key risk factors of city road traffic accident factor, using traffic flow data, the dynamic characteristics of traffic flow on the impact on traffic accident risk, is the main way to accurate judgments the road traffic system and improve the security level of the whole city for city road traffic accidents. In addition, the city road network analysis and grasp the traffic flow The law, improve the real-time traffic flow prediction, reliability and adaptability is the hot topic of research, provide an important guarantee for the data traffic accident warning. Research on the logical level according to the traffic flow characteristics of traffic flow forecasting traffic accident warning, focus on how to analyze the spatial correlation characteristics of city traffic flow and how to use the traffic spatial cross-correlation realization of city road network multi section traffic flow forecasting and how to use the traffic flow of traffic accident early warning and other issues. This paper studies the main problems in the process, and the corresponding theory and method are verified by numerical examples or instances of the scene, the main research achievements embodied in the the following aspects: (1) the complex network theory put forward a reflection of road traffic flow spatial correlation of city road based on Through the network modeling and analysis method. By modeling the city road traffic system based on complex network theory, used to reflect the complexity of road section traffic flow correlation distribution. The city road traffic system is a system composed of sections of the traffic flow space correlation generated network. The network node represents road section, the existence of edges between nodes depends on the node of traffic flow on the spatial correlation degree. According to the sequence of city road traffic network construction, this paper presented a geographical weighted PageRank algorithm (GWPA), is used to determine the importance of road section, to provide support for the division of traffic flow correlation of spatial clustering. (2) from the complex network community discovery of road section traffic flow distribution spatial correlation angle, this paper proposes a GWPA-k-means based on city traffic space The correlation analysis method. The method was improved from the two aspects of the traditional K-means algorithm for the initial population selection problem, proposed an improved algorithm based on clustering center density, the initial population was established using the node GWPA and the length of the shortest path problem; for node similarity matrix is constructed, based on node GWPA value and put forward a weighted signal transmission method for measuring similarity of nodes in the network, to provide the basis for k-means clustering. The experimental results show that the GWPA-k-means method can better reveal the city road section traffic spatial correlation model. (3) based on the spatial correlation characteristics of traffic flow, proposes a community discovery and long term memory neural network multi section traffic flow forecasting method based on GWPA-k-means algorithm. The method according to the classification of road traffic flow spatial cross-correlation mode will The road network is divided into several regions, located between the same area of road traffic flow strong spatial correlation; then for each regional network, a two-dimensional matrix sequence formed by road traffic flow sequence as input, selection of traffic flow data using the spatial and temporal characteristics of learning and long-term memory neural network, so as to realize the multi section traffic flow short term forecasting. According to the prediction model parameters, this paper puts forward a model of adaptive parameter optimization algorithm based on orthogonal genetic algorithm. The experimental results show that the city traffic flow spatial correlation to enhance traffic flow prediction accuracy, this method resulted in good adaptability in case of missing data in sensor fault, has strong robustness. (4) with traffic flow data as the foundation, proposed the city road traffic accident early warning method based on the traffic flow factors. In real-time traffic flow. State on the basis of analysis of influence factors on the traffic flow of traffic accident risk, and separately from the traffic accident detection and traffic accident risk prediction on the two aspects of early warning methods of traffic accidents. The relationship in a domain division perspective to clarify the traffic flow and traffic accident risk variables, gives the concept of traffic safety domain; put forward a feature analysis based on sequence forward selection and principal component extraction algorithm for feature variables extraction significant traffic accident risk; proposes a security domain least squares support vector machine estimation method based on real-time detection of traffic accident, traffic safety domain. In addition, the real-time traffic flow data based on the traffic safety domain and reliability analysis theory, puts forward a traffic reliability model, the model can also to the traffic accident risk assessment and macro statistics Real-time risk prediction to a single traffic accident.

【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：U491

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