﻿ 公路客运量的回归分析和研究预测 Regression Analysis and Prediction of Highway Passenger Volume

Vol.06 No.02(2017), Article ID:19759,10 pages
10.12677/ASS.2017.62020

Regression Analysis and Prediction of Highway Passenger Volume

Dandan Shen

Shanghai Maritime University, Shanghai

Received: Feb. 1st, 2017; accepted: Feb. 19th, 2017; published: Feb. 22nd, 2017

ABSTRACT

With the development of economy and the increase of living standard, China has paid more attention on the infrastructure of highway transportation. This paper applies the related data of the passenger capacity of the highway transportation from 1981 to 2015 to analyze the factors influenced the passenger capacity of the highway transportation. It takes the population, GDP, agricultural GDP and the civil car ownership as the independent variables, and the passenger capacity of the highway transportation as the dependent variable to establish the multivariate regression model with MATLAB. At the same time, the rationality of the regress model is also analyzed in this essay. We have fitted various factors of the multivariate regress model in consideration of the complexity, verified and improved the multivariate regress model with stepwise regression in order to enhance the scientificity and accuracy of the model. In the final step, the passenger capacity of the highway transportation from 2011 to 2015 has been calculated by the regress model of univariate and multivariate, and a comparison has been made between the calculated date and the actual data which aims to analyze the difference and errors.

Keywords:Passenger Capacity of the Highway Traffic, Regression Model, Stepwise Regression, The Civil Car Ownership

1. 引言

2. 变量的选取

3. 数据

1981~2015年我国公路客运量、总人口、国内生产总值、工农业总产值、民用载客汽车拥有量数据见表1

4. 公路客运量与各自变量的多元回归模型

Table 1. Data of highway passenger volume, total population and gross domestic product, gross output value of industry and agriculture, civil passenger car ownership in 1981-2015

(1)

(2)

4.1. 多元线性回归模型

(3)

Table 2. Coefficients, confidence intervals and statistics of regression models

(4)

4.2. 模型的进一步改进

(5)

4.3. 改进后的模型与参考文献结果的比较

(6)

Table 3. Coefficients, confidence intervals and statistics of improved regression model

Figure 1. Schematic diagram of residual error

Figure 2. Sketch map of residual error I

Figure 3. Sketch map of residual error II

Figure 4. Sketch map of residual error III

Figure 5. Sketch map of residual error IV

Figure 6. Stepwise regression process I

Figure 7. Stepwise regression process II

Figure 8. Stepwise regression process III

Table 4. Five year value of each index

Table 5. Compared with the original data values, the fitted value of highway passenger trans- port volume in recent five years

5. 模拟实验

5.1. 多元线性回归方程实证分析

5.2. 预测结果的分析

Regression Analysis and Prediction of Highway Passenger Volume[J]. 社会科学前沿, 2017, 06(02): 151-160. http://dx.doi.org/10.12677/ASS.2017.62020

1. 1. 何晓群, 刘文卿. 应用回归分析[M]. 北京: 中国人民大学出版社, 2011: 1-15.

2. 2. 景滨杰. 回归分析法在经济预测中的应用浅析[J]. 山西经济管理干部学院学报, 2004, 12(3): 32-34.

3. 3. 李柏年, 吴礼斌. MATLAB数据分析方法[M]. 北京: 机械工业出版社, 2012: 33-80.

4. 4. 乔向明. 2003~2005年我国公路客运量预测分析[J]. 山东交通学院学报, 2003, 11(1): 26-29.

5. 5. 庞皓. 计量经济学[M]. 北京: 科学出版社, 2010: 72-103.

6. 6. 王松桂, 陈敏, 陈立苹. 线性统计模型——线性回归与方差分析[M]. 北京: 高等教育出版社, 1999.

7. 7. 高惠璇. 应用多元统计分析[M]. 北京: 北京大学出版社, 2005: 22-45.

8. 8. 胡明伟, 史其信. 探讨回归分析在交通工程应用中的若干问题[J]. 公路交通科技, 2009, 19(1): 68-71.

9. 9. 杨巍, 张莉莉. 逐步回归分析在经济林产品需求预测中的应用[J]. 林业经济研究报告, 2009(8): 74-76.

10. 10. 贾俊平, 何晓群, 金勇进. 统计学[M]. 北京: 中国人民大学出版社, 2012: 265-317.