發(fā)布時(shí)間：2018-10-12 12:22

【摘要】：隨著經(jīng)濟(jì)的不斷發(fā)展和日益加劇的城市化進(jìn)程,汽車(chē)保有量逐年增加,現(xiàn)代城市交通面臨越來(lái)越多的問(wèn)題,如交通擁堵、交通污染和交通事故等。這些問(wèn)題對(duì)人們的出行造成許多影響,降低人們出行效率,使城市道路面臨越來(lái)越大的交通壓力。自由式路網(wǎng)城市由于地形限制,交通現(xiàn)象尤為復(fù)雜,城市道路交通擁堵嚴(yán)重,擁堵疏散較為困難,為緩解自由式路網(wǎng)交通擁堵,對(duì)其交通流參數(shù)進(jìn)行短時(shí)預(yù)測(cè),并根據(jù)預(yù)測(cè)結(jié)果對(duì)交通擁堵?tīng)顩r進(jìn)行判定識(shí)別,發(fā)出實(shí)時(shí)的交通擁堵預(yù)報(bào)信息非常重要。本論文以自由式路網(wǎng)交通流為研究對(duì)象,介紹交通流基本特征參數(shù),分析交通流參數(shù)的采集技術(shù)及其適用范圍,研究交通信息與處理方法。研究自由式路網(wǎng)交通擁堵的交通流特性和時(shí)空分布特征,對(duì)自由式路網(wǎng)交通擁堵原因進(jìn)行深入分析。對(duì)自由式路網(wǎng)來(lái)說(shuō),交通流參數(shù)的相關(guān)度和城市道路斷面之間的相對(duì)位置有關(guān),距離較遠(yuǎn),相關(guān)性比較低,同樣距離近的道路斷面由于某些道路交通控制措施(如單向交通或交叉口禁左等)相關(guān)性不一定強(qiáng)。據(jù)此,本文提出基于空間相關(guān)性的自由式路網(wǎng)交通參數(shù)短時(shí)預(yù)測(cè)模型。通過(guò)分析和比較單一預(yù)測(cè)方法的優(yōu)缺點(diǎn)和適用范圍,確定采用基于多維新息算法的ARIMA預(yù)測(cè)方法和RBF神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)方法對(duì)自由式路網(wǎng)交通參數(shù)進(jìn)行短時(shí)預(yù)測(cè),并將這兩種預(yù)測(cè)方法根據(jù)最優(yōu)權(quán)分配進(jìn)行組合,利用組合模型對(duì)交通參數(shù)進(jìn)行短時(shí)預(yù)測(cè),并通過(guò)算例驗(yàn)證本論文研究的組合模型對(duì)交通參數(shù)短時(shí)預(yù)測(cè)效果。根據(jù)路網(wǎng)交通流分布的時(shí)間和空間特性,本文將自由式路網(wǎng)交通擁堵?tīng)顩r分為四個(gè)等級(jí),即暢通、較擁堵、擁堵、堵塞。分別從交叉口、路段兩方面選定評(píng)價(jià)指標(biāo),形成指標(biāo)體系。采用基于層次熵定權(quán)的模糊綜合評(píng)判模型對(duì)預(yù)測(cè)得到的交通流參數(shù)進(jìn)行評(píng)價(jià),識(shí)別交通擁堵?tīng)顩r,進(jìn)而發(fā)出交通擁堵實(shí)時(shí)預(yù)報(bào)信息。本文提出的交通擁堵預(yù)報(bào)方法可以及時(shí)預(yù)報(bào)交通擁堵的發(fā)生,對(duì)交通參與者交通出行路線(xiàn)的選擇和交通管理者對(duì)交通擁堵疏散決策均有一定的參考價(jià)值。
[Abstract]:With the continuous development of economy and the increasing process of urbanization, the number of cars is increasing year by year. Modern urban traffic is facing more and more problems, such as traffic congestion, traffic pollution and traffic accidents. These problems have a lot of impact on people's travel, reduce people's travel efficiency, and make urban roads face more and more traffic pressure. Because of the terrain restriction, the traffic phenomenon is especially complex in the free-type road network city. The traffic congestion is serious and the evacuation is difficult. In order to alleviate the traffic congestion of the free road network, the traffic flow parameters of the freeway road network are forecasted in a short time. According to the prediction results, it is very important to identify the traffic congestion and send out real-time traffic congestion forecast information. In this paper, the basic characteristic parameters of traffic flow are introduced, the collection technology of traffic flow parameters and its applicable scope are analyzed, and the traffic information and processing methods are studied. The characteristics of traffic flow and space-time distribution of free road network traffic congestion are studied, and the causes of free road network traffic congestion are analyzed. For free road networks, the correlation of traffic flow parameters is related to the relative position between urban road sections, the distance is relatively long, the correlation is relatively low. Because of some road traffic control measures (such as one-way traffic or intersection forbidden left), the correlation of the same close road section is not necessarily strong. On the basis of this, this paper presents a short-time prediction model of traffic parameters of freestyle road network based on spatial correlation. By analyzing and comparing the advantages and disadvantages of the single forecasting method and its application scope, the ARIMA forecasting method based on the multidimensional innovation algorithm and the RBF neural network forecasting method are adopted to predict the traffic parameters of the freestyle road network in a short time. The two forecasting methods are combined according to the optimal weight allocation, and the combined model is used to predict the traffic parameters in a short time, and an example is given to verify the effect of the combined model on the short-term prediction of traffic parameters. According to the time and space characteristics of road network traffic flow distribution, this paper divides the free road network traffic congestion into four grades, that is, unblocked, more congested, and blocked. The evaluation index is selected from two aspects of intersection and section, and the index system is formed. The fuzzy comprehensive evaluation model based on hierarchical entropy weight is used to evaluate the traffic flow parameters, identify the traffic congestion, and send out the real-time traffic congestion forecast information. The traffic congestion forecasting method proposed in this paper can forecast the occurrence of traffic congestion in time. It has certain reference value for traffic participants' route choice and traffic manager's decision of traffic congestion evacuation.
【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：U491

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本文編號(hào)：2266096