本文選題：交通擁堵 + 信號控制�。� 參考：《華南理工大學》2015年碩士論文

【摘要】：交通擁堵已經(jīng)成為世界各國高度關(guān)注和亟待解決的問題。由交通擁堵導致的交通能耗、環(huán)境污染,是我國城市面臨的極其嚴重的“城市病”之一。交通擁堵識別可以起到預防和緩解城市交通擁堵的作用,因此對其研究具有重要意義。針對城市道路交通擁堵特征,結(jié)合信號控制交叉口的特點,本文主要研究討論了基于貝葉斯決策的信號控制交叉口擁堵識別方法,貝葉斯訓練樣本集的更新方法,基于樸素貝葉斯的信號控制交叉口交通擁堵狀態(tài)識別系統(tǒng)的設(shè)計。對于信號控制交叉口擁堵識別方法的研究,以貝葉斯基礎(chǔ)理論和樸素貝葉斯分類器模型為基礎(chǔ),本文提出了一種基于樸素貝葉斯決策的信號控制交叉口擁堵識別方法,該方法把交通擁堵的識別看作是一個不確定性的分類問題,把交通狀態(tài)分成暢通、擁擠和擁堵三種狀態(tài),將交通流量、占有率和排隊長度比作為判別參數(shù),通過學習在暢通、擁擠和擁堵三種狀態(tài)下的歷史數(shù)據(jù),生成貝葉斯分類器,然后利用分類器對實時采集到的數(shù)據(jù)進行分類,從而識別交通狀態(tài)。貝葉斯分類器模型是以歷史訓練樣本的概率表來決策的,所以最佳的訓練樣本自然能夠決定分類器最優(yōu)的分類趨向。訓練樣本的準備是貝葉斯分類的基礎(chǔ)工作,但在實際中很難獲取完備的訓練樣本,且僅僅依靠一成不變的歷史數(shù)據(jù)來識別交通狀態(tài),全面性也不夠的。基于此,本文提出了一種增量學習方法來更新訓練樣本,并對識別算法進行改進,即按照一定的規(guī)則將分類好的數(shù)據(jù)添加到訓練集中,動態(tài)更新訓練集,豐富訓練信息,使得擁堵的識別結(jié)果更加可靠。通過VISSIM仿真獲取的數(shù)據(jù)對貝葉斯算法進行了分析和評價,算法的誤判率為6.92%,表明算法對信號控制交叉口的擁堵識別具有可行性和實用性。最后,以理論引導實踐,介紹了信號控制交叉口交通狀態(tài)識別系統(tǒng)的總體架構(gòu),構(gòu)建了基于樸素貝葉斯方法的信號控制交叉口交通擁堵狀態(tài)識別系統(tǒng),包括功能設(shè)計和數(shù)據(jù)庫設(shè)計。在C#編程語言集成開發(fā)環(huán)境下,采用結(jié)構(gòu)化的設(shè)計思想,通過軟件實現(xiàn)所闡述的功能,實現(xiàn)交通狀態(tài)的識別。
[Abstract]:Traffic congestion has become a serious concern and urgent problem in the world. Traffic energy consumption and environmental pollution caused by traffic congestion are one of the most serious "urban diseases" faced by cities in China. Traffic congestion identification plays an important role in preventing and alleviating urban traffic congestion, so it is of great significance to study it. According to the traffic congestion characteristics of urban roads and the characteristics of signal-controlled intersections, this paper mainly discusses the congestion identification method of signal-controlled intersections based on Bayesian decision, and the updating method of Bayesian training sample set. Design of signal-controlled intersection traffic congestion recognition system based on naive Bayes. On the basis of Bayesian theory and naive Bayesian classifier model, this paper presents a new method of congestion identification for signal-controlled intersection based on naive Bayesian decision. In this method, traffic congestion identification is regarded as an uncertain classification problem, traffic state is divided into three states: unblocked, congested and congested, traffic flow, occupation rate and queue length ratio are taken as discriminant parameters, and the traffic flow, occupancy ratio and queue length ratio are used as discriminant parameters. The historical data of congestion and congestion are used to generate Bayesian classifier and then the real-time collected data are classified by classifier to identify the traffic state. Bayesian classifier model is based on the probability table of historical training samples, so the best training samples can naturally determine the optimal classification trend of classifiers. The preparation of training samples is the basic work of Bayesian classification, but it is very difficult to obtain complete training samples in practice, and only rely on historical data to identify traffic state, comprehensive is not enough. Based on this, an incremental learning method is proposed to update the training samples, and the recognition algorithm is improved, that is, the classified data is added to the training set according to certain rules, the training set is updated dynamically, and the training information is enriched. Make the identification of congestion more reliable. The Bayesian algorithm is analyzed and evaluated by the data obtained by VISSIM simulation. The error rate of the algorithm is 6.92. it shows that the algorithm is feasible and practical to identify the congestion of signal-controlled intersection. Finally, the paper introduces the overall framework of signal-controlled intersection traffic state recognition system, and constructs a signal-controlled intersection traffic congestion recognition system based on naive Bayesian method. Including function design and database design. In the C # programming language integrated development environment, the structure design idea is adopted, and the function described is realized by software to realize the recognition of traffic state.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.23

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