【摘要】：隨著機(jī)動(dòng)車輛的迅速增加,城市交通擁堵問題日益加劇,制約著經(jīng)濟(jì)社會(huì)的發(fā)展,給居民的出行帶來了很大的不變,解決城市交通擁堵變得尤為重要。提升交通信號(hào)的控制效率已成為解決交通擁堵的一種有效方法。本文針對(duì)目前交叉口信號(hào)控制方法和控制子區(qū)劃分方法的不足,結(jié)合智能控制理論,在交叉口信號(hào)控制方案的選擇、交叉口重要性評(píng)估、控制子區(qū)的動(dòng)態(tài)劃分等方面進(jìn)行了探討分析,具體的研究?jī)?nèi)容如下:(1)系統(tǒng)地介紹了交通信號(hào)控制理論和模糊控制理論,在交叉口信號(hào)控制中分別采用了單級(jí)模糊控制器和兩級(jí)模糊控制器,研究了不同交通流下兩種控制器的控制效果,通過分析控制器輸入變量的選擇,確定了不同交通流下控制器結(jié)構(gòu)的配置。(2)針對(duì)在單交叉口信號(hào)控制中控制模型單一導(dǎo)致不能很好適應(yīng)交通流變化的問題,提出了一種將單級(jí)模糊控制和兩級(jí)模糊控制兩種策略相結(jié)合的組合控制模型,并利用SOM神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)了兩種控制策略的切換。針對(duì)兩級(jí)模糊控制器控制規(guī)則和隸屬度函數(shù)人工設(shè)定的不足,利用混沌遺傳算法優(yōu)化兩級(jí)模糊控制器參數(shù)。同時(shí)為了保證控制的實(shí)時(shí)性,結(jié)合滑動(dòng)時(shí)間窗,根據(jù)實(shí)時(shí)采集數(shù)據(jù)實(shí)現(xiàn)交通狀態(tài)的快速識(shí)別和兩級(jí)模糊控制器參數(shù)在線優(yōu)化。通過實(shí)例仿真,對(duì)提出的組合優(yōu)化控制模型進(jìn)行了驗(yàn)證。(3)以交叉口連接度、高峰車流量和車道占有率作為評(píng)價(jià)指標(biāo),應(yīng)用熵權(quán)TOPSIS法對(duì)交叉口的重要性進(jìn)行分析,給出了交叉口重要性評(píng)價(jià)的具體步驟,實(shí)現(xiàn)了城市區(qū)域路網(wǎng)的關(guān)鍵節(jié)點(diǎn)選擇。(4)針對(duì)多數(shù)交通控制子區(qū)劃分方法只考慮兩交叉口關(guān)聯(lián)度而忽略了關(guān)鍵交叉口的重要性的問題,提出了基于關(guān)鍵交叉口交通控制子區(qū)的動(dòng)態(tài)劃分方法,以關(guān)鍵交叉口為起點(diǎn)遍歷四周交叉口,利用模糊推理求得兩相鄰交叉口之間的關(guān)聯(lián)度,在此基礎(chǔ)上,通過計(jì)算協(xié)調(diào)交叉口與關(guān)鍵交叉口的周期差確定控制子區(qū)的劃分,并結(jié)合實(shí)例對(duì)提出的子區(qū)劃分方法進(jìn)行了分析研究。
[Abstract]:With the rapid increase of motor vehicles, the problem of urban traffic congestion is becoming more and more serious, which restricts the development of economy and society, and brings great invariance to the travel of residents, so it becomes more and more important to solve the urban traffic congestion. Improving the control efficiency of traffic signals has become an effective method to solve traffic congestion. In view of the deficiency of the current signal control method and the control sub-area division method of intersection, combining with the intelligent control theory, the selection of signal control scheme and the importance evaluation of intersection are discussed in this paper. The dynamic division of the control sub-area is discussed and analyzed. The specific research contents are as follows: (1) the traffic signal control theory and fuzzy control theory are introduced systematically. The single stage fuzzy controller and two stage fuzzy controller are used in the intersection signal control. The control effect of the two controllers under different traffic flow is studied, and the selection of the input variables of the controller is analyzed. The configuration of controller structure under different traffic flow is determined. (2) aiming at the problem of single control model in single intersection signal control, it can not adapt to traffic flow change well. This paper presents a combined control model which combines single-stage fuzzy control and two-level fuzzy control, and realizes the switching of the two control strategies using SOM neural network. Aiming at the deficiency of manual setting of control rules and membership function of two-stage fuzzy controller, the parameters of two-stage fuzzy controller are optimized by chaos genetic algorithm. At the same time, in order to ensure the real-time control, combined with sliding time window, according to the real-time data acquisition, the fast identification of traffic state and the on-line optimization of two-stage fuzzy controller parameters are realized. Through the example simulation, the combined optimal control model is verified. (3) the importance of intersection is analyzed by using entropy weight TOPSIS method, taking intersection connectivity, peak traffic flow and lane occupancy as evaluation indexes. The concrete steps of the importance evaluation of intersections are given, and the selection of key nodes in urban regional road network is realized. (4) considering only the correlation degree of two intersections in most traffic control subareas, the importance of key intersections is neglected. This paper presents a dynamic division method based on traffic control sub-area of key intersection. It uses key intersection as the starting point to traverse around intersection and use fuzzy inference to obtain the correlation degree between two adjacent intersections. The division of the control sub-area is determined by calculating the period difference between the coordinated intersection and the key intersection, and the proposed subarea division method is analyzed and studied with an example.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491.54

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 程海鵬;湯自安;湯e，

本文編號(hào)：2124736