【摘要】：為更好地對道路客運量未來發(fā)展趨勢進(jìn)行預(yù)測,建立更加精確的預(yù)測模型,在分析主要預(yù)測方法的基礎(chǔ)上,提出一種基于規(guī)劃求解的組合預(yù)測模型�；诨疑Ｐ汀⒁辉貧w和指數(shù)平滑三種預(yù)測方法,建立了以歷年預(yù)測值加權(quán)之和與實際值的差值的絕對值之和最小為目標(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,通過對目標(biāo)函數(shù)值的變化值進(jìn)行觀測,當(dāng)目標(biāo)函數(shù)的變化值出現(xiàn)拐點時,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),建立了道路旅客運輸客運量組合預(yù)測模型。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,目標(biāo)函數(shù)變化值出現(xiàn)拐點,以此確定了迭代次數(shù);傳統(tǒng)3種預(yù)測方法的絕對誤差分別是1.26,0.48和2.98,基于規(guī)劃求解的組合預(yù)測模型的絕對誤差是0.12,預(yù)測精度更高、誤差更小,并且該組合預(yù)測模型操作方便,可減少單個模型預(yù)測的不確定性,可根據(jù)上述模型對未來道路旅客發(fā)送量進(jìn)行預(yù)測。
[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】： 交通運輸部科學(xué)研究院;
【基金】：交通運輸部建設(shè)科技項目(2015 318 J36 110) 交通運輸戰(zhàn)略規(guī)劃政策研究項目(2016-1-4) 中央級公益性科研院所基本科研業(yè)務(wù)費項目(2016 6110)
【分類號】：U492.413

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