【摘要】：隨著社會(huì)經(jīng)濟(jì)的發(fā)展,交通需求持續(xù)加強(qiáng),交通擁堵、環(huán)境污染、交通事故、能源短缺等各種城市交通問(wèn)題日益嚴(yán)重。交通控制系統(tǒng)從初期的單點(diǎn)定時(shí)控制發(fā)展到如今的區(qū)域自適應(yīng)控制,成為治理城市交通問(wèn)題的重要舉措。近年來(lái),隨著計(jì)算機(jī)技術(shù)、通信技術(shù)、人工智能等現(xiàn)代高科技技術(shù)的不斷發(fā)展,交通誘導(dǎo)系統(tǒng)也應(yīng)用于城市交通系統(tǒng)中,誘導(dǎo)效果也在不斷提高。交通控制系統(tǒng)和交通誘導(dǎo)系統(tǒng)作為智能交通系統(tǒng)的重要組成部分,管理目標(biāo)都是城市路網(wǎng)的交通流,二者既相互聯(lián)系、又相互影響。交通控制系統(tǒng)和交通誘導(dǎo)系統(tǒng)獨(dú)立運(yùn)行時(shí)各自都能取得很好的效果,但是從系統(tǒng)的角度看,并不一定能取得最優(yōu)的效果。如何將交通控制與交通誘導(dǎo)協(xié)同運(yùn)行,達(dá)到系統(tǒng)最優(yōu)的效果,實(shí)現(xiàn)交通管理系統(tǒng)的資源節(jié)約與整合,實(shí)現(xiàn)交通管理手段的時(shí)空互補(bǔ),對(duì)于改善城市交通問(wèn)題具有重要意義。 本文對(duì)交通控制系統(tǒng)、交通誘導(dǎo)系統(tǒng)、交通信息處理等基礎(chǔ)知識(shí)進(jìn)行了綜述,綜合國(guó)內(nèi)外的研究成果,分析了交通控制與交通誘導(dǎo)協(xié)同的關(guān)系,總結(jié)了交通控制與交通誘導(dǎo)協(xié)同的模式。 以均衡路網(wǎng)上的交通流量,消除局部地區(qū)的交通擁堵,讓整個(gè)路網(wǎng)交通系統(tǒng)恢復(fù)到平衡的交通狀態(tài)為核心思想,提出一種基于消除擁堵和區(qū)域內(nèi)總的行程時(shí)間最小的雙目標(biāo)協(xié)同優(yōu)化模型。模型從加權(quán)平均飽和度和加權(quán)平均飽和度的方差作為切入點(diǎn),判斷協(xié)同小區(qū)內(nèi)是否存在路段或交叉口的擁堵,如存在則通過(guò)調(diào)整綠信比,均衡路網(wǎng)交通流量消除擁堵,否則以協(xié)同小區(qū)的總的行程時(shí)間盡可能小為目標(biāo)進(jìn)行綠信比的調(diào)整和路網(wǎng)流量的分配。 針對(duì)提出的雙目標(biāo)協(xié)同模型,分別設(shè)計(jì)了以消除擁堵和優(yōu)化行程時(shí)間為目標(biāo)的求解算法。其中,消除擁堵的求解算法采用小步長(zhǎng)的試算優(yōu)化不斷調(diào)整信號(hào)配時(shí)參數(shù),達(dá)到消除擁堵的目的,優(yōu)化行程時(shí)間的求解算法采用遺傳算法進(jìn)行求解,得到控制與誘導(dǎo)的控制變量。最后通過(guò)實(shí)例仿真,驗(yàn)證了模型及算法的有效性。 最后對(duì)論文進(jìn)行了總結(jié)和展望�？偨Y(jié)了本文的主要研究成果,并對(duì)未來(lái)的研究提出了改進(jìn)方案。
[Abstract]:With the development of social economy, traffic demand is continuously strengthened, traffic congestion, environmental pollution, traffic accidents, energy shortage and other urban traffic problems are becoming more and more serious. Traffic control system has developed from the initial single-point timing control to the regional adaptive control, which has become an important measure to deal with urban traffic problems. In recent years, with the development of modern high-tech technology, such as computer technology, communication technology, artificial intelligence and so on, traffic guidance system has been applied in urban transportation system, and the effect of guidance has been improved. Traffic control system (TCS) and traffic guidance system (TSS), as an important part of intelligent transportation system (its), manage the traffic flow of urban road network, both of which are related to each other and interact with each other. Both the traffic control system and the traffic guidance system can achieve good results when they are running independently, but from the point of view of the system, the optimal results are not always achieved. How to coordinate traffic control and traffic guidance to achieve the optimal effect of the system, realize the resource saving and integration of traffic management system, and realize the space-time complementary of traffic management means is of great significance to improve the urban traffic problem. This paper summarizes the basic knowledge of traffic control system, traffic guidance system, traffic information processing, and analyzes the relationship between traffic control and traffic guidance coordination. The mode of coordination between traffic control and traffic guidance is summarized. The core idea is to balance the traffic flow on the road network, eliminate traffic congestion in local areas, and restore the whole road network traffic system to a balanced traffic state. A two-objective cooperative optimization model based on eliminating congestion and minimizing the total travel time in a region is proposed. Based on the variance of weighted average saturation and weighted average saturation, the model determines whether there is congestion at road sections or intersections in synergetic communities, and if it exists, it can balance the traffic flow to eliminate congestion by adjusting the green letter ratio. Otherwise, the total travel time of the cooperative community is as small as possible to adjust the green to credit ratio and to allocate the network flow. Aiming at the proposed two-objective cooperative model, the algorithms aiming at eliminating congestion and optimizing travel time are designed respectively. Among them, the algorithm of eliminating congestion adopts small step size to optimize the parameters of signal timing, so as to eliminate congestion, and the algorithm of optimizing travel time is solved by genetic algorithm. Control and induced variables are obtained. Finally, the effectiveness of the model and algorithm is verified by an example. Finally, the paper is summarized and prospected. The main research results of this paper are summarized, and the improvement scheme for future research is put forward.
【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U491

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