本文關(guān)鍵詞： 關(guān)鍵路口 關(guān)聯(lián)度 子區(qū)劃分 組合模型 預(yù)測 集成分類器 狀態(tài)識別　出處：《浙江大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著科學(xué)技術(shù)的不斷發(fā)展以及人民生活水平的不斷提高,交通擁堵問題越來越普遍。交通擁堵不僅會降低人民的生活質(zhì)量,同時也會影響經(jīng)濟發(fā)展并帶來資源浪費、環(huán)境污染等各種問題,因此,緩解交通擁堵刻不容緩。智能交通系統(tǒng)是提高交通控制水平和管理效率的重要手段,而區(qū)域交通狀態(tài)識別是實現(xiàn)智能交通系統(tǒng)的前提,因而對交通狀態(tài)識別技術(shù)的研究具有重要的理論意義和實用價值。 本文針對我國城市交通特點,利用自動控制理論、人工智能原理和交通工程技術(shù)對交通控制子區(qū)劃分、短時交通流預(yù)測以及子區(qū)交通狀態(tài)識別等技術(shù)進行了深入研究。具體內(nèi)容如下： (1)分別提出了靜態(tài)關(guān)鍵路口和動態(tài)關(guān)鍵路口的選取方法。靜態(tài)關(guān)鍵路口主要根據(jù)主干道原則、路口飽和度原則、流量原則、時間規(guī)律原則以及交通部門的經(jīng)驗進行選取。動態(tài)關(guān)鍵路口主要通過路口關(guān)鍵度進行選取,為此研究了基于層次分析法的路口關(guān)鍵度計算方法。 (2)提出了基于改進遺傳算法的交通控制子區(qū)劃分方法。首先設(shè)計了一種基于模糊邏輯的關(guān)聯(lián)度計算方法,然后基于關(guān)鍵路口和路口關(guān)聯(lián)度建立了交通控制子區(qū)劃分模型,并利用改進遺傳算法進行模型求解,實現(xiàn)了交通控制子區(qū)的劃分。 (3)提出了基于變權(quán)重組合模型的短時交通流組合預(yù)測方法。首先分析了交通流特征以及影響交通流的主要因素,然后根據(jù)交通流特征設(shè)計了基于卡爾曼濾波模型和神經(jīng)網(wǎng)絡(luò)模型的變權(quán)重組合預(yù)測模型,其中兩個單一模型的權(quán)值通過上一步的預(yù)測誤差進行實時調(diào)整,為了增加模型的平穩(wěn)性,還引入了慣性因子。 (4)提出了基于FCM集成分類器的區(qū)域交通狀態(tài)識別方法。首先將當(dāng)前時刻交通流數(shù)據(jù)與交通流預(yù)測數(shù)據(jù)的融合數(shù)據(jù)作為交通狀態(tài)識別輸入數(shù)據(jù),然后分別提出了基于模糊C均值和基于FCM集成分類器的交通狀態(tài)識別方法,交通狀態(tài)識別的輸出為“低飽和”、“中飽和”和“準(zhǔn)飽和”三種狀態(tài)。
[Abstract]:With the development of science and technology and the improvement of people's living standard, traffic congestion is becoming more and more common. Traffic congestion will not only reduce people's quality of life, but also affect economic development and bring waste of resources. Therefore, it is urgent to alleviate traffic congestion. Intelligent Transportation system (its) is an important means to improve traffic control level and management efficiency, and the recognition of regional traffic condition is the prerequisite to realize its. Therefore, the research of traffic state recognition technology has important theoretical significance and practical value. According to the characteristics of urban traffic in China, this paper uses the theory of automatic control, the principle of artificial intelligence and traffic engineering technology to divide the sub-areas of traffic control. Short-time traffic flow prediction and sub-area traffic state recognition are studied in depth. The specific contents are as follows:. 1) the selection methods of static and dynamic key junctions are put forward respectively. The static key junctions are mainly based on the principle of main road, the principle of saturation and the principle of flow. The principle of time law and the experience of traffic department are selected. The key degree of intersection is chosen by dynamic key intersection. Therefore, the calculation method of intersection criticality based on analytic hierarchy process (AHP) is studied. (2) A traffic control sub-area partition method based on improved genetic algorithm is proposed. Firstly, a method of calculating the correlation degree based on fuzzy logic is designed, and then the traffic control sub-area partition model is established based on the correlation degree of key intersection and intersection. The improved genetic algorithm is used to solve the model and the traffic control sub-area is divided. In this paper, a short-term traffic flow combination forecasting method based on variable weight combination model is proposed. Firstly, the characteristics of traffic flow and the main factors affecting traffic flow are analyzed. Then, according to the traffic flow characteristics, the variable weight combined forecasting model based on Kalman filter model and neural network model is designed, in which the weights of two single models are adjusted in real time through the prediction error of the previous step. In order to increase the stability of the model, the inertia factor is also introduced. Firstly, the fusion data of current traffic flow data and traffic flow prediction data are taken as the input data of traffic state recognition. Then a traffic state recognition method based on fuzzy C-means and FCM integrated classifier is proposed. The output of traffic state recognition is "low saturation", "medium saturation" and "quasi-saturation".
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495

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