本文關鍵詞： 多智能體(Multi-Agent) 車路協(xié)同(CVIS) 自適應控制 多智能體遺傳算法　出處：《廈門理工學院》2015年碩士論文　論文類型：學位論文

【摘要】：目前對城市道路交叉口信號燈的優(yōu)化控制的研究集中在對單個交叉口的信號配時優(yōu)化,雖然僅研究單點交叉口的優(yōu)化控制降低了信號配時的復雜性,但過于簡化問題使研究成果不能很好地應用于實際。本文提出了基于Agent的自適應交通信號協(xié)同控制方法,該方法通過優(yōu)化道路交叉口信號控制和改善車輛駕駛行為,實現(xiàn)區(qū)域交通系統(tǒng)基于Agent的協(xié)同運行。首先,本文設計了基于Multi-Agent的車路協(xié)同系統(tǒng)模型(Cooperative Vehicles-Infrastructure System,CVIS),為自適應交通信號協(xié)同控制提供實時可靠的交通流數(shù)據(jù)。CVIS架構中每臺車和各重要路口的信號燈均為多智能體,包含路側終端、車載終端和行人探測器,通過無線通信網(wǎng)絡實現(xiàn)人-車-路的信號互聯(lián),全方位感知周圍環(huán)境信息。其次,在CVIS基礎上設計了一種基于Agent的自適應協(xié)同交通信號控制模型ACTAM(Adaptive and Cooperative Traffic Light Agent Model,ACTAM),該模型利用智能體的自治性和自組織性提高對道路交叉口控制的自適應性,并依據(jù)從CVIS獲得的實時交通流數(shù)據(jù)信息通過增強學習實時優(yōu)化交通控制策略。再次,采用多智能體遺傳算法(Multi-Agent genetic algorithm,MAGA)對控制目標進行優(yōu)化,這種全局優(yōu)化協(xié)調(diào)控制的策略使各個路口Agent在未知其他路口信號控制方案時,彼此協(xié)調(diào)合作并實現(xiàn)對區(qū)域交通的自適應協(xié)同控制。最后,結合廈門市交警大隊提供的數(shù)據(jù),使用交通仿真軟件Trans Modeler對廈門市政府區(qū)域和廈門市廈禾路BRT沿線進行仿真,結果表明本文方法能夠有效降低交叉口的平均延誤時間和停車次數(shù),可以提高路口控制單元的適應能力并有效緩解交通擁堵的現(xiàn)狀。
[Abstract]:At present, the research on the optimal control of signal lights at urban road intersections is focused on the signal timing optimization of single intersection, although the complexity of signal timing is reduced by only studying the optimal control of single intersection. However, the problem of oversimplification can not be applied to practice well. In this paper, an adaptive traffic signal cooperative control method based on Agent is proposed, which can optimize the traffic signal control and improve the vehicle driving behavior. To realize the cooperative operation of regional transportation system based on Agent. First of all, In this paper, a cooperative Vehicles-Infrastructure system CVIS system model based on Multi-Agent is designed, which provides real-time and reliable traffic flow data for adaptive traffic signal collaborative control. In the framework of CVIS, each vehicle and every important intersection signal light is multi-agent, including roadside terminal. Vehicle terminal and pedestrian detector, through wireless communication network to achieve human-vehicle-road signal interconnection, omni-directional perception of the surrounding environment information. Secondly, Based on CVIS, an adaptive collaborative traffic signal control model based on Agent, ACTAM(Adaptive and Cooperative Traffic Light Agent Model, is designed. The model makes use of the autonomy and self-organization of agents to improve the adaptability of road intersection control. According to the real-time traffic flow information obtained from CVIS, the real-time traffic control strategy is optimized by reinforcement learning. Thirdly, multi-agent genetic algorithm is used to optimize the control target. This global optimal coordinated control strategy enables Agent to coordinate and cooperate with each other in unknown signal control schemes of other intersections and to realize adaptive cooperative control of regional traffic. Finally, combined with the data provided by Xiamen Traffic Police Brigade, The traffic simulation software Trans Modeler is used to simulate the area of Xiamen Municipal Government and the BRT line of Xiamen Xiahe Road. The results show that this method can effectively reduce the average delay time and the number of stops at the intersection. It can improve the adaptability of the intersection control unit and effectively alleviate the current situation of traffic congestion.
【學位授予單位】：廈門理工學院
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.54;TP273

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