【摘要】：為更好地對(duì)道路客運(yùn)量未來發(fā)展趨勢(shì)進(jìn)行預(yù)測(cè),建立更加精確的預(yù)測(cè)模型,在分析主要預(yù)測(cè)方法的基礎(chǔ)上,提出一種基于規(guī)劃求解的組合預(yù)測(cè)模型。基于灰色模型、一元回歸和指數(shù)平滑三種預(yù)測(cè)方法,建立了以歷年預(yù)測(cè)值加權(quán)之和與實(shí)際值的差值的絕對(duì)值之和最小為目標(biāo)函數(shù),以權(quán)重系數(shù)非負(fù)和權(quán)重系數(shù)之和為1的規(guī)劃求解模型。在權(quán)重計(jì)算過程中,目標(biāo)函數(shù)值隨著迭代次數(shù)的增加逐漸減少,通過對(duì)目標(biāo)函數(shù)值的變化值進(jìn)行觀測(cè),當(dāng)目標(biāo)函數(shù)的變化值出現(xiàn)拐點(diǎn)時(shí),定義了組合權(quán)重迭代次數(shù)的確定方法。選取更具有代表意義的"百城百站"旅客發(fā)送量為基礎(chǔ)數(shù)據(jù),建立了道路旅客運(yùn)輸客運(yùn)量組合預(yù)測(cè)模型。在規(guī)劃求解過程中利用Excel中的"規(guī)劃求解"功能。試驗(yàn)結(jié)果表明:隨著迭代次數(shù)的增加,目標(biāo)函數(shù)變化值逐漸減小,當(dāng)?shù)螖?shù)為7時(shí),目標(biāo)函數(shù)變化值出現(xiàn)拐點(diǎn),以此確定了迭代次數(shù);傳統(tǒng)3種預(yù)測(cè)方法的絕對(duì)誤差分別是1.26,0.48和2.98,基于規(guī)劃求解的組合預(yù)測(cè)模型的絕對(duì)誤差是0.12,預(yù)測(cè)精度更高、誤差更小,并且該組合預(yù)測(cè)模型操作方便,可減少單個(gè)模型預(yù)測(cè)的不確定性,可根據(jù)上述模型對(duì)未來道路旅客發(fā)送量進(jìn)行預(yù)測(cè)。
[Abstract]:In order to forecast the future development trend of road passenger traffic, a more accurate forecasting model is established. Based on the analysis of the main forecasting methods, a combined forecasting model based on programming solution is proposed. Based on the grey model, univariate regression and exponential smoothing three forecasting methods, the objective function is established, which is the sum of the absolute value of the difference between the weighted sum of the predicted value over the years and the difference between the actual value and the sum of the absolute value as the objective function. The programming solution model is based on the sum of the non-negative weight coefficient and the weight coefficient 1. In the process of weight calculation, the value of objective function decreases gradually with the increase of iteration times. By observing the change value of objective function value, when the change value of objective function appears inflection point, the method of determining the iteration number of combined weight is defined. Based on the data of "Baicheng and 100 stations" which is more representative, a combined forecast model of passenger volume for road passenger transport is established. In the process of planning and solving, the function of "programming solving" in Excel is used. The experimental results show that with the increase of the number of iterations, the change value of the objective function decreases gradually. When the number of iterations is 7, the inflection point of the change value of the objective function appears, and the number of iterations is determined. The absolute errors of the three traditional prediction methods are 1.26,0.48 and 2.98, respectively. The absolute error of the combined forecasting model based on programming solution is 0.12. the prediction precision is higher and the error is smaller, and the combined forecasting model is easy to operate. The uncertainty of single model prediction can be reduced, and the future road passenger traffic can be predicted according to the above model.
【作者單位】： 交通運(yùn)輸部科學(xué)研究院;
【基金】：交通運(yùn)輸部建設(shè)科技項(xiàng)目(2015 318 J36 110) 交通運(yùn)輸戰(zhàn)略規(guī)劃政策研究項(xiàng)目(2016-1-4) 中央級(jí)公益性科研院所基本科研業(yè)務(wù)費(fèi)項(xiàng)目(2016 6110)
【分類號(hào)】：U492.413

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張欣;;組合預(yù)測(cè)模型在上海內(nèi)河港口吞吐量預(yù)測(cè)中的應(yīng)用[J];水運(yùn)工程;2007年04期

2 張維朋;;組合預(yù)測(cè)模型在寧波港口集裝箱吞吐量的預(yù)測(cè)研究[J];科技通報(bào);2012年05期

3 楊新倉(cāng);李楓;;組合預(yù)測(cè)模型在公路客運(yùn)量預(yù)測(cè)中的應(yīng)用[J];山西建筑;2012年04期

4 郁小鋒;余靜;;組合預(yù)測(cè)模型在港口物流量預(yù)測(cè)中的應(yīng)用[J];中國(guó)水運(yùn)(下半月);2008年06期

5 林安東;基于誤差絕對(duì)值之加權(quán)和最小的組合預(yù)測(cè)模型及其應(yīng)用[J];上海海運(yùn)學(xué)院學(xué)報(bào);2000年03期

6 孫慧慧;;組合預(yù)測(cè)模型在北京市汽車擁有量預(yù)測(cè)中的應(yīng)用[J];淮陰師范學(xué)院學(xué)報(bào)(自然科學(xué)版);2013年04期

7 童明榮;薛恒新;林琳;;基于最優(yōu)組合預(yù)測(cè)模型的港口集裝箱吞吐量預(yù)測(cè)[J];技術(shù)經(jīng)濟(jì);2006年12期

8 張凱;盧鄒穎;;短時(shí)車流量組合預(yù)測(cè)模型[J];南京信息工程大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年05期

9 劉啟文;林鋼;林吾思;;基于MATLAB的組合模型在港口吞吐量預(yù)測(cè)中的應(yīng)用[J];水運(yùn)工程;2008年11期

10 張?jiān)瓶?張曉宇;;組合預(yù)測(cè)模型在寧波港集裝箱吞吐量預(yù)測(cè)中的應(yīng)用[J];中國(guó)水運(yùn)(下半月);2008年01期

相關(guān)會(huì)議論文 前1條

1 謝正文;孔凡玉;胡毅夫;;基于熵權(quán)的沉降組合預(yù)測(cè)模型及應(yīng)用[A];第一屆中國(guó)水利水電巖土力學(xué)與工程學(xué)術(shù)討論會(huì)論文集（下冊(cè)）[C];2006年

相關(guān)博士學(xué)位論文 前2條

1 于志恒;基于智能理論的交通流量組合預(yù)測(cè)模型研究[D];東北師范大學(xué);2016年

2 趙川;重載交通對(duì)高速公路橋梁的疲勞影響研究[D];河北工業(yè)大學(xué);2015年

相關(guān)碩士學(xué)位論文 前2條

1 王超;組合預(yù)測(cè)模型在集裝箱生成量預(yù)測(cè)中的應(yīng)用研究[D];合肥工業(yè)大學(xué);2009年

2 陳珂;傳感器網(wǎng)絡(luò)技術(shù)在隧道變形監(jiān)測(cè)中的應(yīng)用研究[D];西南交通大學(xué);2013年

，

本文編號(hào)：2437528