【摘要】：隨著交通流的日益增大及復(fù)雜化,城市路網(wǎng)擁堵問題越來越嚴(yán)重,現(xiàn)有的智能交通控制難以提高城域交通系統(tǒng)的整體效率。將主動(dòng)引導(dǎo)交通流、均衡交通資源的誘導(dǎo)系統(tǒng)和被動(dòng)疏導(dǎo)交通流的控制系統(tǒng)有機(jī)結(jié)合是解決城市交通問題的有效途徑。如何構(gòu)建這樣的智能交通系統(tǒng)優(yōu)化模型及其優(yōu)化算法成為當(dāng)前的研究熱點(diǎn)和關(guān)鍵技術(shù)。針對(duì)現(xiàn)有交通控制系統(tǒng)存在的問題,本文采用了將基于進(jìn)化多目標(biāo)優(yōu)化的控制與基于蟻群算法優(yōu)化的誘導(dǎo)有機(jī)結(jié)合的交通調(diào)控模型及其優(yōu)化方法,構(gòu)建了單路口多目標(biāo)優(yōu)化控制模型、路口間的協(xié)調(diào)機(jī)制及車輛誘導(dǎo)模型,能有效均衡交通負(fù)載,提高城域路網(wǎng)的交通效率。主要研究工作如下:(1)針對(duì)現(xiàn)有交通控制系統(tǒng)難以有效兼顧各種指標(biāo)及根據(jù)實(shí)時(shí)交通狀態(tài)高效調(diào)節(jié)控制信號(hào)配時(shí)方案,構(gòu)建了單路口多目標(biāo)優(yōu)化控制模型,采用改進(jìn)的進(jìn)化多目標(biāo)優(yōu)化算法實(shí)現(xiàn)交通信號(hào)優(yōu)化。路口控制優(yōu)化模型以單位時(shí)間內(nèi)通過的車輛數(shù)盡可能的多、一個(gè)周期內(nèi)的平均時(shí)耗盡可能的少為優(yōu)化目標(biāo)。該模型可以根據(jù)實(shí)時(shí)的道路車流量信息,高效地調(diào)節(jié)自身配時(shí)方案,并能給交通決策者提供多種偏好的配時(shí)方案。為了適應(yīng)交通控制系統(tǒng)多目標(biāo)優(yōu)化的需求,提出了一種多子種群并行進(jìn)化的非支配排序多目標(biāo)優(yōu)化算法,仿真測(cè)試實(shí)驗(yàn)表明,該算法具有較高的時(shí)效性,較強(qiáng)的對(duì)pareto前沿面的探索能力和保持種群多樣性的能力。(2)針對(duì)現(xiàn)有區(qū)域多路口協(xié)調(diào)方式中,各路口控制耦合度高,協(xié)調(diào)控制復(fù)雜,實(shí)時(shí)性差,并且對(duì)路口擁堵預(yù)判能力差等問題,構(gòu)建了多路口協(xié)調(diào)控制機(jī)制,該機(jī)制通過調(diào)節(jié)路口間的車流量與道路飽合車流量的比值,來協(xié)調(diào)多個(gè)路口的運(yùn)行。根據(jù)該協(xié)調(diào)機(jī)制的特點(diǎn),采用了模糊控制技術(shù)進(jìn)行實(shí)現(xiàn)。仿真驗(yàn)證實(shí)驗(yàn)表明,該協(xié)調(diào)機(jī)制能減少交通擁堵的響應(yīng)時(shí)間,快速協(xié)調(diào)各個(gè)路口的信號(hào)控制,提高區(qū)域交通效率。(3)針對(duì)現(xiàn)有誘導(dǎo)系統(tǒng)較少考慮道路上的動(dòng)態(tài)代價(jià)和出行者的起始地與目的地等問題,構(gòu)建了基于多種指標(biāo)的車輛誘導(dǎo)模型,并采用改進(jìn)的蟻群算法實(shí)現(xiàn)對(duì)出行路徑的規(guī)劃。車輛誘導(dǎo)模型優(yōu)化指標(biāo)由三部分組成:起始地與目的地間的靜態(tài)路徑長(zhǎng)度、該路徑上通過路口總的延時(shí)轉(zhuǎn)換得到的等效代價(jià)、在道路上運(yùn)行時(shí)產(chǎn)生的動(dòng)態(tài)代價(jià)。該優(yōu)化模型在力求用戶路徑最優(yōu)的同時(shí),能盡量實(shí)現(xiàn)道路車輛的均衡分布。為了滿足誘導(dǎo)系統(tǒng)路徑規(guī)劃的需求,提出了一種有偏好的蟻群算法,該算法通過偏好的設(shè)置和局部最優(yōu)跳出機(jī)制,提高了全局收索能力和效率,仿真測(cè)試實(shí)驗(yàn)驗(yàn)證了算法對(duì)誘導(dǎo)系統(tǒng)路徑尋優(yōu)有較高效能。
[Abstract]:With the increasing and complication of traffic flow, the problem of urban road network congestion is becoming more and more serious, and the existing intelligent traffic control is difficult to improve the overall efficiency of urban transportation system. It is an effective way to solve urban traffic problems by combining active guiding traffic flow, balancing the guidance system of traffic resources with the control system of passive traffic flow. How to build such an intelligent transportation system optimization model and its optimization algorithm has become the current research hotspot and key technology. Aiming at the problems existing in the existing traffic control systems, this paper adopts a traffic control model and its optimization method, which combines evolutionary multi-objective control with ant colony optimization. The multi-objective optimal control model of single intersection the coordination mechanism between intersections and the vehicle guidance model can effectively balance the traffic load and improve the traffic efficiency of the metropolitan road network. The main research work is as follows: (1) in view of the existing traffic control system is difficult to take into account all kinds of indicators effectively and according to the real-time traffic state efficient regulation control signal timing scheme, a multi-objective optimal control model for a single intersection is constructed. An improved evolutionary multi-objective optimization algorithm is used to optimize traffic signals. The optimization model of intersection control is based on the maximum number of vehicles passing through the unit time and the possible decrease of the average time depletion in a period. Based on the real-time traffic flow information, the model can efficiently adjust its own timing scheme and provide traffic decision makers with a variety of preferred timing schemes. In order to meet the needs of multi-objective optimization in traffic control systems, a multi-objective optimization algorithm with parallel evolution of multi-subpopulations is proposed. The simulation results show that the algorithm has high time-efficiency. Strong ability to explore the pareto frontier and maintain population diversity. (2) in the existing regional multi-intersection coordination mode, the intersection control coupling degree is high, coordination control is complex and real-time is poor. To solve the problem of poor predetermination ability of traffic congestion, the coordinated control mechanism of multi-intersection is constructed. The mechanism coordinates the operation of multiple intersections by adjusting the ratio of traffic flow between intersections and traffic flow. According to the characteristics of the coordination mechanism, fuzzy control technology is adopted. The simulation results show that the coordination mechanism can reduce the response time of traffic congestion and quickly coordinate the signal control of each intersection. (3) considering the dynamic cost on the road and the origin and destination of the travelers, a vehicle guidance model based on multiple indexes is constructed. The improved ant colony algorithm is used to plan the travel path. The optimization index of the vehicle guidance model consists of three parts: the static path length between the initial location and the destination, the equivalent cost obtained by the total intersections delay conversion on the route, and the dynamic cost when running on the road. The optimization model can achieve the equilibrium distribution of road vehicles as much as possible while striving for the optimal path of the user. In order to meet the needs of path planning of induced systems, a preferred ant colony algorithm is proposed, which improves the ability and efficiency of global cable collection through preference setting and local optimal jump out mechanism. The simulation results show that the algorithm is effective for path optimization of induced systems.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U495;TP18

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本文編號(hào)：2196073