本文選題：視頻聯(lián)網(wǎng) + 多智能體��； 參考：《北京交通大學(xué)》2017年博士論文

【摘要】：隨著車流檢測技術(shù)的不斷發(fā)展,傳統(tǒng)檢測方式難以滿足實際應(yīng)用需求。近年來,將智能視頻監(jiān)控技術(shù)應(yīng)用于交通信息獲取與處理,解決交通擁堵問題,已成為智能交通系統(tǒng)中的一項關(guān)鍵技術(shù)。目前雖已建立起了較為完善的智能交通系統(tǒng),但各子控制系統(tǒng)之間難以進(jìn)行有效的數(shù)據(jù)融合和信息共享,只能進(jìn)行簡單的信息采集和處理,并沒有通過視頻聯(lián)網(wǎng)共享和交通控制策略結(jié)合起來,致使系統(tǒng)之間的聯(lián)動控制機(jī)制沒有形成。本文主要以國家自然基金項目(F030209) “基于Agent和演化博弈的城市交通信號區(qū)域聯(lián)動控制協(xié)調(diào)運(yùn)行研究”為依托,開展對城市交通智能化管理和控制的關(guān)鍵技術(shù)進(jìn)行研究。利用視頻識別相關(guān)的技術(shù)手段,實現(xiàn)對交通流參數(shù)的實時提取,實時判別交通狀態(tài),結(jié)合agent技術(shù),針對相鄰交叉口交通流的關(guān)聯(lián)性,通過相鄰交叉口間的信息交互,對控制小區(qū)進(jìn)行調(diào)整,并了解其協(xié)調(diào)任務(wù)的簡易度和緊急度,及時采取聯(lián)動控制措施。首先,根椐交通的實時性需求,構(gòu)建了基于虛擬線圈的交通參數(shù)提取模型,描述了車輛檢測和車輛跟蹤的實現(xiàn)過程,分析了參數(shù)提取的重點要素,指出了車輛檢測和跟蹤是交通參數(shù)提取的重點。其次,根椐交通參數(shù)的時空變化規(guī)律分析,提出基于模糊認(rèn)知圖的交通狀態(tài)快速識R%與躍遷轉(zhuǎn)變模型,構(gòu)建了交通參數(shù)關(guān)聯(lián)關(guān)系圖,指出了躍遷轉(zhuǎn)變的內(nèi)部和外部影響,分析了躍遷轉(zhuǎn)變的過程。然后,根據(jù)基于系統(tǒng)動力學(xué)的擁擠傳播模型對交通擁堵的產(chǎn)生及傳播規(guī)律進(jìn)行分析,應(yīng)用節(jié)點收縮法確定關(guān)鍵節(jié)點,得到一個關(guān)鍵節(jié)點重要度的排序。根據(jù)交通擁擠擴(kuò)散規(guī)律,以關(guān)鍵節(jié)點為控制小區(qū)中心,以最短路徑阻抗作為約束條件,確定控制小區(qū)的范圍。根據(jù)交通關(guān)聯(lián)度和相似度對節(jié)點和關(guān)聯(lián)路徑進(jìn)行劃分,提出了基于節(jié)點收縮法的控制小區(qū)動態(tài)調(diào)整與優(yōu)化模型。接著,根據(jù)多Agent協(xié)調(diào)聯(lián)動控制不僅要考慮自身的運(yùn)行效果,而且考慮自已排放車流對下游交叉口的影響,將每個路口看作一個Agent,建立基于遺傳算法的信號優(yōu)化模型;根據(jù)交通任務(wù)的簡易度和緊急度,建立基于模糊理論的Agent協(xié)調(diào)控制選擇模型;根據(jù)直接信任協(xié)調(diào)和間接信任協(xié)調(diào)確定協(xié)調(diào)團(tuán)隊,構(gòu)建虛擬控制校區(qū),提出了基于多Agent的協(xié)調(diào)聯(lián)動控制模型。最后,根據(jù)對原有系統(tǒng)的更新和改進(jìn),并要求Agent不同功能之間的互聯(lián)方式、信息傳遞和數(shù)據(jù)共享,以集成的思想,設(shè)計了一個基于多Agent的城市交通協(xié)調(diào)聯(lián)動控制體系。通過對視頻識別和多Agent的協(xié)調(diào)聯(lián)動控制關(guān)鍵技術(shù)的研究,可以有效提高提取交通參數(shù)的速度和準(zhǔn)確度,并增強(qiáng)交通信號控制能力和覆蓋范圍,實現(xiàn)城市交通實時、多點以及協(xié)同控制的目的,對緩解城市交通擁堵具有指導(dǎo)意義和參考價值。
[Abstract]:With the continuous development of vehicle flow detection technology, the traditional detection method is difficult to meet the needs of practical applications. In recent years, the application of intelligent video surveillance technology to traffic information acquisition and processing, to solve the traffic congestion problem, has become a key technology in the intelligent transportation system. At present, although the intelligent transportation system has been established, it is difficult to carry out effective data fusion and information sharing among the sub-control systems, so it can only carry out simple information collection and processing. There is no combination of video sharing and traffic control strategy, so the linkage control mechanism between the systems has not been formed. Based on the National Natural Fund project F030209) "study on coordinated Operation of Urban Traffic signal Regional coordinated Control based on Agent and Evolutionary Game", this paper studies the key technologies of intelligent management and control of urban traffic. By using the related technology of video recognition, the real-time extraction of traffic flow parameters and the real-time identification of traffic status are realized. Combined with agent technology, according to the correlation of traffic flow at adjacent intersections, the information exchange between adjacent intersections is achieved. Adjust the control area and understand the simplicity and urgency of the coordination task, and take the linkage control measures in time. Firstly, according to the real-time demand of traffic, a traffic parameter extraction model based on virtual coil is constructed, the realization process of vehicle detection and vehicle tracking is described, and the key elements of parameter extraction are analyzed. It is pointed out that vehicle detection and tracking are the key points of traffic parameter extraction. Secondly, based on the analysis of the temporal and spatial variation of traffic parameters, a model of fast recognition of traffic state based on fuzzy cognitive map is put forward, and the correlation diagram of traffic parameters is constructed, and the internal and external effects of transition transition are pointed out. The transition process is analyzed. Then, according to the congestion propagation model based on system dynamics, the generation and propagation rules of traffic congestion are analyzed, and the key nodes are determined by node contraction method, and a ranking of the importance of key nodes is obtained. According to the law of traffic congestion diffusion, the key nodes are taken as the control center and the shortest path impedance is taken as the constraint condition to determine the range of the control cell. According to the traffic correlation degree and the similarity degree, the dynamic adjustment and optimization model of the control cell based on the node contraction method is proposed. Then, according to the multi- coordinated linkage control, we should not only consider the effect of their own operation, but also consider the effect of their own traffic flow on the downstream intersection, and consider each intersection as an agent, and establish a signal optimization model based on genetic algorithm. According to the simplicity and urgency of traffic task, the Agent coordination control selection model based on fuzzy theory is established, the coordination team is determined according to direct trust coordination and indirect trust coordination, and the virtual control school district is constructed. A coordinated linkage control model based on multiple Agent is proposed. Finally, according to the update and improvement of the original system and the requirement of interconnecting mode, information transmission and data sharing among different functions of Agent, an urban traffic coordination and linkage control system based on multiple Agent is designed with the idea of integration. Through the research on the key technology of video recognition and coordinated linkage control of multiple Agent, the speed and accuracy of extracting traffic parameters can be improved effectively, and the control ability and coverage of traffic signal can be enhanced to realize real-time urban traffic. The purpose of multi-point and cooperative control is of guiding significance and reference value to alleviate urban traffic congestion.
【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】：U495;TP391.41

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