發(fā)布時間：2018-09-08 21:05

【摘要】：城市有限的道路資源難以承載交通量的快速增長,導致交通擁堵問題的出現(xiàn),交通擁堵預測是解決交通擁堵問題的重要步驟之一。但由于影響交通系統(tǒng)的因素復雜繁多,且各種交通參數(shù)具有較強隨機性和不確定性,使得交通擁堵預測研究難以開展,預測成功率及可靠性往往不高,針對這一問題,本文借鑒馬爾可夫理論及灰色預測理論,構(gòu)建了適用于交通擁堵預測的灰色GM(1,1)-加權(quán)馬爾可夫預測模型,并將該模型應(yīng)用于實例研究中。具體研究過程如下： 首先,在回顧國內(nèi)外研究現(xiàn)狀的基礎(chǔ)上,給出了擁堵的定義、分類、成因和特征。對經(jīng)典的擁堵識別算法和常見的速度預測模型進行了分析； 其次,探討速度預測與擁堵識別的關(guān)系和基于速度的交通擁堵預測的原理,并確定相應(yīng)的速度閾值標準,基于灰色預測理論,結(jié)合馬爾可夫鏈預測原理,建立灰色GM(1,1)-馬爾可夫預測模型用于交通擁堵預測,并在此基礎(chǔ)上對該模型進行加權(quán)改進以獲得更好的預測成功率； 最后,將該模型應(yīng)用于石家莊市主干路——建設(shè)大街的擁堵預測實例研究中,對該路段未來4天內(nèi)6個不同時刻的擁堵狀態(tài)進行了預測識別,并與灰色GM(1,1)預測模型、灰色GM(1,1)-馬爾可夫預測模型的預測結(jié)果相比較。結(jié)果表明,該模型的識別成功率超過66%,優(yōu)于灰色GM(1,1)預測模型和灰色GM(1,1)-馬爾可夫預測模型,從而表明本文所建立的預測模型具有較好的識別準確率及可靠性。
[Abstract]:Limited urban road resources are difficult to support the rapid growth of traffic volume, leading to the emergence of traffic congestion problem, traffic congestion prediction is one of the important steps to solve the traffic congestion problem. However, due to the complexity of the factors affecting the traffic system and the strong randomness and uncertainty of various traffic parameters, it is difficult to carry out the research of traffic congestion prediction, and the success rate and reliability of traffic congestion prediction are not always high. Based on Markov theory and grey prediction theory, a grey GM (1) -weighted Markov forecasting model for traffic congestion prediction is constructed in this paper. The model is applied to a case study. The specific research process is as follows: firstly, the definition, classification, causes and characteristics of congestion are given on the basis of reviewing the current research situation at home and abroad. The classical congestion identification algorithms and common speed prediction models are analyzed. Secondly, the relationship between speed prediction and congestion identification and the principle of traffic congestion prediction based on speed are discussed, and the corresponding speed threshold standard is determined. Based on the grey prediction theory and the Markov chain prediction principle, the grey GM (1k-1) -Markov forecasting model is established for traffic congestion prediction, and the weight of the model is improved to obtain a better prediction success rate. Finally, the model is applied to the case study of traffic congestion prediction on the main road of Shijiazhuang City-Construction Street. The congestion state of this section at 6 different times in the next 4 days is forecasted and identified, and it is compared with the grey GM (1Q1) prediction model. The prediction results of grey GM (1 ~ 1)-Markov model are compared. The results show that the success rate of the model is more than 66, which is superior to the grey GM (1t1) prediction model and the grey GM (1K1) -Markov prediction model, which shows that the prediction model established in this paper has good recognition accuracy and reliability.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U491.265

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本文編號：2231694