【摘要】：隨著城市的不斷發(fā)展，城市交通的矛盾主要表現(xiàn)為城市交通供給不能滿足日益增長的交通需求，城市道路的交通擁堵問題越來越嚴(yán)重，逐漸成為制約城市和諧發(fā)展的一個全球性社會問題，緩解城市道路交通擁堵的工作顯得越來越重要。城市干道是城市交通的動脈，對城市干道交通擁堵狀況進(jìn)行及時、準(zhǔn)確的預(yù)測和識別，有針對性地對擁堵點采取交通控制和誘導(dǎo)等措施，可緩解干道交通瓶頸的擁堵程度，減少交通擁堵帶來的負(fù)面效應(yīng)。因此對城市干道交通擁堵預(yù)警建立科學(xué)有效的方法具有重要的實用價值。 本文利用交通流模型對城市干道交通擁堵在形成、持續(xù)、消散過程中流量、速度和密度之間的變化特性和時空特性進(jìn)行了分析，提出交通擁堵預(yù)警是通過預(yù)測未來時刻干道某一截面的交通流狀態(tài)參數(shù)，識別出該截面未來時刻的交通擁堵狀況，預(yù)先采取有針對性的緩堵策略。在分析了交通流狀態(tài)參數(shù)的基礎(chǔ)上，對比分析了幾種主要的交通信息數(shù)據(jù)采集技術(shù)，并對預(yù)處理交通信息數(shù)據(jù)的方法進(jìn)行了探討。 城市干道交通狀態(tài)與相鄰截面的交通狀態(tài)密切相關(guān)，本文提出了基于多點狀態(tài)參數(shù)的交通擁堵預(yù)警方法，建立預(yù)測的關(guān)鍵截面與相關(guān)的多個檢測截面的交通狀態(tài)相關(guān)模型，通過現(xiàn)狀和歷史交通流狀態(tài)參數(shù)數(shù)據(jù)序列預(yù)測下一時段關(guān)鍵截面的交通流狀態(tài)。采用ARIMA時間序列預(yù)測模型和遺傳算法改進(jìn)優(yōu)化后的BP神經(jīng)網(wǎng)絡(luò)預(yù)測方法，建立了交通狀態(tài)參數(shù)預(yù)測的線性組合模型，提出了基于最小誤差平方和、等權(quán)和熵值法求解組合模型的權(quán)重值，并通過算例驗證了基于最小誤差平方和求權(quán)重的預(yù)測方法的預(yù)測效果最優(yōu)。 最后，以平均速度、飽和度和平均延誤作為城市干道交通擁堵的評價指標(biāo)，將交通擁堵劃分為暢通、輕微擁堵、擁堵和嚴(yán)重?fù)矶滤膫�等級。提出對交通狀態(tài)參數(shù)的預(yù)測結(jié)果采用改進(jìn)的模糊綜合評價方法，實現(xiàn)對交通擁堵程度的識別，并發(fā)出交通擁堵預(yù)警信息，，從而達(dá)到對城市干道交通擁堵的預(yù)警。 論文提出的交通擁堵預(yù)警方法能夠及時有效的預(yù)警交通擁堵的發(fā)生，可用于智能交通系統(tǒng)中的交通狀態(tài)預(yù)警和交通誘導(dǎo)。
[Abstract]:With the continuous development of the city, the contradiction of urban traffic mainly shows that the supply of urban traffic can not meet the increasing traffic demand, and the traffic congestion problem of urban roads is becoming more and more serious. It has gradually become a global social problem that restricts the harmonious development of cities, and it is becoming more and more important to alleviate urban road traffic congestion. Urban trunk roads are the arteries of urban traffic. Timely, accurate prediction and identification of traffic congestion on urban trunk roads, and targeted traffic control and guidance measures can alleviate the congestion degree of traffic bottlenecks in trunk roads. Reduce the negative effects of traffic congestion. Therefore, it is of great practical value to establish a scientific and effective method for traffic congestion warning on urban trunk roads. In this paper, traffic flow model is used to analyze the changing characteristics and space-time characteristics of traffic congestion between flow, velocity and density in the process of formation, persistence and dissipation of urban trunk road traffic congestion. It is proposed that traffic congestion warning is based on the prediction of traffic flow state parameters of a certain section of the main road in the future, the identification of traffic congestion at the future time of the section, and the adoption of a targeted strategy of slowing down the traffic congestion in advance. Based on the analysis of traffic flow state parameters, several main traffic information data acquisition techniques are compared and analyzed, and the methods of preprocessing traffic information data are discussed. The traffic state of urban trunk roads is closely related to the traffic state of adjacent sections. A traffic congestion warning method based on multi-point state parameters is proposed in this paper. The current and historical traffic flow state data series are used to predict the traffic flow state of critical sections in the next period. Using ARIMA time series prediction model and genetic algorithm to improve the optimized BP neural network prediction method, the linear combination model of traffic state parameter prediction is established, and the minimum error square sum is proposed. The method of equal weight and entropy is used to solve the weight value of the combined model, and an example is given to verify the optimal prediction effect of the prediction method based on the sum of least error square. Finally, with the average speed, saturation and delay as the evaluation index of traffic congestion, traffic congestion is divided into four grades: unblocked, slight, congested and severely congested. In this paper, an improved fuzzy comprehensive evaluation method is proposed to predict the traffic state parameters, which can recognize the traffic congestion degree and issue the traffic congestion warning information, so as to achieve the traffic congestion warning of the urban trunk roads. The traffic congestion warning method proposed in this paper can alert traffic congestion in time and effectively, and can be used in traffic state early warning and traffic guidance in intelligent transportation system (its).
【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495;U491.265

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 陳華友;熵值法及其在確定組合預(yù)測權(quán)系數(shù)中的應(yīng)用[J];安徽大學(xué)學(xué)報(自然科學(xué)版);2003年04期

2 張敬淦;北京交通擁堵癥結(jié)分析[J];城市問題;2004年05期

3 王曉原;吳磊;張開旺;張敬磊;;非參數(shù)小波算法的交通流預(yù)測方法[J];系統(tǒng)工程;2005年10期

4 楊兆升,楊慶芳,馮金巧;基于模糊綜合推理的道路交通事件識別算法[J];公路交通科技;2003年04期

5 姜桂艷;常安德;牛世峰;;基于車牌識別數(shù)據(jù)的交通擁堵識別方法[J];哈爾濱工業(yè)大學(xué)學(xué)報;2011年04期

6 王宏杰,林良明,徐大淦,顏國正;基于改進(jìn)BP網(wǎng)交通流動態(tài)時序預(yù)測算法的研究[J];交通與計算機;2001年03期

7 楚楊杰;陳春紅;劉昭;王雄;宋兵;;道路交通擁堵自動檢測的改進(jìn)加州算法及仿真[J];數(shù)學(xué)雜志;2012年04期

8 羅小強;陳寬民;張同芬;;城市快速路交通擁擠事件檢測與程度辨識[J];長安大學(xué)學(xué)報(自然科學(xué)版);2010年03期

9 吳志周;范宇杰;馬萬經(jīng);;基于灰色神經(jīng)網(wǎng)絡(luò)的點速度預(yù)測模型[J];西南交通大學(xué)學(xué)報;2012年02期

10 賀國光,馬壽峰,李宇;基于小波分解與重構(gòu)的交通流短時預(yù)測法[J];系統(tǒng)工程理論與實踐;2002年09期

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