【摘要】：世界經(jīng)濟(jì)發(fā)展已經(jīng)進(jìn)入全新時(shí)期,全球化經(jīng)濟(jì)正在日益向前推進(jìn),各經(jīng)濟(jì)體之間相互融合,相互影響。全球經(jīng)濟(jì)賴以生存的全球物流網(wǎng)絡(luò)正在逐步建設(shè),不斷完善。我國(guó)正在組建快速、高效、全面的物流網(wǎng)絡(luò)。目前我國(guó)已基本建設(shè)完成多形式、多渠道、全方位的交通運(yùn)輸物流網(wǎng),這個(gè)運(yùn)輸物流網(wǎng)包括公路物流、鐵路物流、水運(yùn)物流、遠(yuǎn)洋物流和管道物流等多種物流運(yùn)輸方式,其中公路物流以其機(jī)動(dòng)靈活、快速高效、運(yùn)量大的優(yōu)勢(shì)成為物流網(wǎng)絡(luò)中的主力軍。國(guó)家在公路建設(shè)投資的控制和公路發(fā)展戰(zhàn)略的制定中需要充分考慮公路帶來的效益,公路效益來自公路運(yùn)輸,公路運(yùn)輸需要公路貨運(yùn)量的支撐,準(zhǔn)確的預(yù)測(cè)結(jié)果對(duì)指導(dǎo)未來公路建設(shè)布局、未來經(jīng)濟(jì)發(fā)展有重要的意義�；谏鲜霰尘昂湍康�,本文主要研究的問題是運(yùn)用Rough Set理論和灰色理論相結(jié)合的方法分析、預(yù)測(cè)公路貨運(yùn)量。首先,本文在國(guó)內(nèi)外研究現(xiàn)狀的基礎(chǔ)上,充分分析了公路貨運(yùn)量預(yù)測(cè)模型的特點(diǎn),結(jié)合這些特點(diǎn)進(jìn)行了相關(guān)理論知識(shí)的學(xué)習(xí),為本文預(yù)測(cè)模型的選擇奠定基礎(chǔ);其次,從宏觀和微觀兩個(gè)方面對(duì)公路貨運(yùn)量的影響因素進(jìn)行說明,建立較為完善的指標(biāo)體系,運(yùn)用灰色變權(quán)聚類和Rough Set理論進(jìn)行影響因素的分析,根據(jù)獲取規(guī)則預(yù)測(cè)公路貨運(yùn)量。具體來說,對(duì)指標(biāo)進(jìn)行數(shù)據(jù)統(tǒng)計(jì),生成信息表,利用灰色變權(quán)聚類方法判別頻率權(quán),將信息表、公路貨運(yùn)量增長(zhǎng)率、頻率權(quán)結(jié)合生成決策表,利用Rough Set理論對(duì)決策表進(jìn)行分析,提取規(guī)則,運(yùn)用規(guī)則對(duì)公路貨運(yùn)量的增長(zhǎng)率進(jìn)行預(yù)測(cè);接著,對(duì)傳統(tǒng)灰色Verhulst模型進(jìn)行改進(jìn),得到無偏灰色Verhulst模型。模型改進(jìn)過程中運(yùn)用到的思想為無偏GM(1,1)模型直接建模法,采取的方法為對(duì)原始序列作倒數(shù)生成,運(yùn)用新生成的序列建立模型,并對(duì)傳統(tǒng)灰色Verhulst模型和無偏灰色Verhulst模型自身誤差進(jìn)行分析;最后,通過實(shí)例驗(yàn)證說明模型改進(jìn)方法的可行性以及改進(jìn)后模型預(yù)測(cè)公路貨運(yùn)量的適用性。實(shí)例為蘭州至中川2009-2015年公路貨運(yùn)量的模擬預(yù)測(cè),分別運(yùn)用GM(1,1)、傳統(tǒng)灰色Verhulst模型、無偏灰色Verhulst模型三種模型進(jìn)行對(duì)比分析。本論文的結(jié)論主要包括兩方面:一方面,運(yùn)用優(yōu)勢(shì)粗糙集理論對(duì)決策表進(jìn)行約簡(jiǎn)、提取規(guī)則,預(yù)測(cè)公路貨運(yùn)量;另一方面,無偏灰色Verhulst模型消除自身固有的偏差,提高公路貨運(yùn)量的預(yù)測(cè)精度,說明改進(jìn)后模型的可行性與適用性。
[Abstract]:The development of the world economy has entered a new period, and the global economy is advancing day by day. The global logistics network, on which the global economy depends, is gradually being built and perfected. China is building a fast, efficient and comprehensive logistics network. At present, our country has completed a multi-form, multi-channel, all-dimensional transportation logistics network, which includes road logistics, railway logistics, waterborne logistics, ocean logistics and pipeline logistics, and so on. Among them, highway logistics has become the main force in logistics network with its advantages of flexibility, speed and efficiency and large volume of transportation. In the control of highway construction investment and the formulation of highway development strategy, the state should take full account of the benefits brought by the highway. The highway benefit comes from the highway transportation, and the highway transportation needs the support of the highway freight volume. Accurate prediction results are of great significance to guide the layout of future highway construction and economic development in the future. Based on the above background and purpose, the main problem of this paper is to use the method of combining Rough Set theory and grey theory to forecast highway freight volume. First of all, on the basis of domestic and foreign research status, this paper fully analyzes the characteristics of highway freight volume forecasting model, combines these characteristics to study the relevant theoretical knowledge, and lays a foundation for the selection of this prediction model. This paper explains the influencing factors of highway freight volume from macro and micro aspects, establishes a relatively perfect index system, analyzes the influencing factors by using grey variable weight clustering and Rough Set theory, and predicts the highway freight volume according to the acquisition rules. Concretely speaking, the index data is counted, the information table is generated, the frequency weight is judged by the grey variable weight clustering method, the information table, the increase rate of highway freight volume and the frequency right are combined to generate the decision table, and the decision table is analyzed by using Rough Set theory. The rules are extracted to predict the growth rate of highway freight volume, and the traditional grey Verhulst model is improved to obtain the unbiased grey Verhulst model. The idea used in the process of model improvement is the direct modeling method of unbiased GM (1K1) model. The method adopted is the reciprocal generation of the original sequence and the establishment of the model by using the newly generated sequence. The error of the traditional grey Verhulst model and unbiased grey Verhulst model is analyzed. Finally, the feasibility of the improved model and the applicability of the improved model to forecast highway freight volume are verified by an example. An example is given for the simulation and prediction of highway freight volume from Lanzhou to Zhongchuan in 2009-2015. Three models, GM (1 / 1), traditional grey Verhulst model and unbiased grey Verhulst model, are used to carry out comparative analysis. The conclusion of this paper mainly includes two aspects: on the one hand, the advantage rough set theory is used to reduce the decision table, extract the rules, and predict the highway freight volume; on the other hand, the unbiased grey Verhulst model eliminates the inherent deviation. The feasibility and applicability of the improved model are illustrated by improving the forecasting accuracy of highway freight volume.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U492.313;N941.5

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本文編號(hào)：2250176