本文關鍵詞： 智能交通 流量采集 模糊神經網絡 流量預測 信號控制　出處：《浙江工業(yè)大學》2014年碩士論文　論文類型：學位論文

【摘要】：隨著社會經濟的高速發(fā)展,汽車的普及率也在不斷地提高。汽車數(shù)量的日益增多,導致城市道路資源變得十分匱乏,道路擁堵正成為近年來各大中城市面臨的最大挑戰(zhàn)之一。因此,提出一種高效的交通流量預測模型,并對路口信號燈做出智能控制,以實現(xiàn)道路資源利用率的最大化和高效化,對于緩解交通擁堵問題將起到十分重要的作用。本文設計了單交叉多相位路口交通信號的控制模型,以交叉路口的交通流量預測和交通信號控制為研究對象,以車輛延誤時間最少為優(yōu)化目標,實現(xiàn)對交通信號配時方案的調整,最終合理控制交通信號燈。主要研究了以下幾方面的內容：(1)分析了智能交通控制系統(tǒng)關鍵技術的工作原理、研究現(xiàn)狀與應用。在此基礎上提出了基于流量預測的單交叉口短時交通預測控制系統(tǒng)的整體結構設計。(2)研究了一種實時流量采集的方法——以背景幀差法為前提的基于YUV色彩模型的目標車輛檢測方法。并以杭州市某路口為例實地驗證其檢測的高效性和低錯誤率。(3)提出了短時交通流量預測的改進方案以及基于模糊神經網絡的交通流量預測模型并利用粒子群(PSO)蟻群(ACO)混合算法對模型進行學習算法的改進。(4)建立了單交叉多相位路口的交通信號模糊神經網絡控制模型,并采用粒子群算法對模型進行訓練,獲得最佳的信號配時方案。從而實現(xiàn)路口各相位信號燈的協(xié)調控制,確保系統(tǒng)的性能指標最優(yōu)。(5)進行了針對所設計的單交叉多相位路口的基于短時流量預測的交通信號控制模型系統(tǒng)的仿真驗證。
[Abstract]:With the rapid development of social economy, the popularization rate of automobile is also increasing. The number of cars is increasing day by day, which leads to the scarcity of urban road resources. Traffic congestion is becoming one of the biggest challenges faced by large and medium-sized cities in recent years. Therefore, an efficient traffic flow forecasting model and intelligent control of intersection signal lights are proposed to maximize the utilization of road resources and achieve high efficiency. It will play an important role in alleviating traffic congestion. In this paper, the traffic signal control model of single intersection and multi-phase intersection is designed, and the traffic flow prediction and traffic signal control of intersection are taken as the research object. Taking the minimum vehicle delay time as the optimization goal, the traffic signal timing scheme can be adjusted and the traffic signal light can be controlled reasonably. The main contents of this paper are as follows: 1) the working principle of the key technology of the intelligent traffic control system is analyzed. On the basis of this, the whole structure design of short-time traffic forecasting control system for single intersection based on flow prediction is proposed. (2) A real-time traffic acquisition method is studied, which is based on background frame difference method. The method of target vehicle detection based on YUV color model is presented. The high efficiency and low error rate of the detection are verified by an example of a intersection in Hangzhou. An improved method of short-term traffic flow prediction and fuzzy neural network are proposed. The traffic flow prediction model and the improved model learning algorithm by particle swarm optimization (PSO) ant colony algorithm (ACO) are used to establish the traffic signal fuzzy neural network control model of single intersection and multi-phase intersection. Particle swarm optimization (PSO) algorithm is used to train the model to obtain the best signal timing scheme, so as to realize the coordinated control of each phase signal light at the intersection. To ensure the optimal performance of the system, the simulation verification of the traffic signal control model system based on short-term flow prediction for the single intersection and multi-phase intersection is carried out.
【學位授予單位】：浙江工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U491.51;TP273

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本文編號：1551870