【摘要】：公路交通是眾多現(xiàn)代化交通運(yùn)輸中發(fā)展最迅速的運(yùn)輸方式,作為綜合交通運(yùn)輸體系的主要組成成分,其具有基礎(chǔ)性的地位,它是推動(dòng)運(yùn)輸體系不斷完善的主導(dǎo)力量。近年來,我國公路事業(yè)蓬勃發(fā)展,如何能夠快速準(zhǔn)確的進(jìn)行交通量的預(yù)測是我們必須面對和解決的問題。預(yù)測模型的選擇直接影響到了我們所需的數(shù)據(jù)資料以及預(yù)測的精度,本文根據(jù)對交通量預(yù)測方法和模型的研究,主要做了以下工作：首先本文分析了公路交通量預(yù)測在公路發(fā)展中的重要性,總結(jié)了公路交通量預(yù)測的發(fā)展趨勢,并分析了常用的各種方法的優(yōu)缺點(diǎn)。探討了影響客運(yùn)量和貨運(yùn)量的影響因素,采用相關(guān)系數(shù)法最終確定與客貨運(yùn)量相關(guān)的參數(shù),分別對客運(yùn)量和貨運(yùn)量進(jìn)行預(yù)測。其次對BP神經(jīng)網(wǎng)絡(luò)和遺傳算法進(jìn)行了相關(guān)的分析和總結(jié),指出了BP神經(jīng)網(wǎng)絡(luò)的缺陷,提出將遺傳算法與BP神經(jīng)網(wǎng)絡(luò)算法相結(jié)合,即GA-BP模型。用遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的權(quán)值和閥值,通過MATLAB建立模型實(shí)施模擬與預(yù)測,得出客貨運(yùn)量的預(yù)測值,并與實(shí)際值作比較,證明了預(yù)測方法的可行性。第三,根據(jù)預(yù)測的客貨運(yùn)量值,轉(zhuǎn)換成標(biāo)準(zhǔn)車輛數(shù),再采用合適的交通量分配方法,將其分配到相應(yīng)路線上,與已有的調(diào)查點(diǎn)數(shù)據(jù)作比較,證明了該種模型用于交通量的預(yù)測的可行性。最后,對采用GA-BP網(wǎng)絡(luò)模型預(yù)測交通量的局限性做了總結(jié)和說明,并也同時(shí)提出了相應(yīng)的問題,為以后繼續(xù)深入探討提供思考。
[Abstract]:Highway traffic is the most rapidly developing transportation mode in many modern transportation. As the main component of the comprehensive transportation system, it has the basic position, and it is the leading force to promote the continuous improvement of the transportation system. In recent years, the highway industry of our country is booming, how to forecast the traffic volume quickly and accurately is the problem that we must face and solve. The choice of prediction model has a direct impact on the data we need and the accuracy of prediction. The main work is as follows: firstly, this paper analyzes the importance of highway traffic volume prediction in highway development, summarizes the development trend of highway traffic volume prediction, and analyzes the advantages and disadvantages of common methods. The influence factors of passenger and freight volume are discussed. The correlation coefficient method is used to determine the parameters related to passenger and freight volume, and the passenger volume and freight volume are forecasted respectively. Secondly, the BP neural network and genetic algorithm are analyzed and summarized, the defects of BP neural network are pointed out, and the combination of genetic algorithm and BP neural network algorithm, that is, GA-BP model, is put forward. The weight and threshold value of BP neural network are optimized by genetic algorithm. The prediction value of passenger and freight volume is obtained by using MATLAB model. The feasibility of the prediction method is proved by comparing with the actual value. Thirdly, according to the predicted passenger and cargo volume, the number of vehicles is converted to standard, and then the appropriate method of traffic flow distribution is used to distribute it to the corresponding route, and compare it with the existing survey data. It is proved that this model is feasible for traffic volume prediction. Finally, the limitations of using GA-BP network model to predict traffic volume are summarized and explained, and the corresponding problems are also put forward, which will provide some thoughts for further discussion in the future.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U491.14

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