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基于模糊线性回归模型的公路货运量预测方法

赵建有,  周孙锋,  崔晓娟,  王高青

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文章导航 > 交通运输工程学报  > 2012 >  12(3): 80-85
赵建有, 周孙锋, 崔晓娟, 王高青. 基于模糊线性回归模型的公路货运量预测方法[J]. 交通运输工程学报, 2012, 12(3): 80-85. doi: 10.19818/j.cnki.1671-1637.2012.03.012
引用本文: 赵建有, 周孙锋, 崔晓娟, 王高青. 基于模糊线性回归模型的公路货运量预测方法[J]. 交通运输工程学报, 2012, 12(3): 80-85. doi: 10.19818/j.cnki.1671-1637.2012.03.012
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ZHAO Jian-you, ZHOU Sun-feng, CUI Xiao-juan, WANG Gao-qing. Predictive method of highway freight volume based on fuzzy linear regression model[J]. Journal of Traffic and Transportation Engineering, 2012, 12(3): 80-85. doi: 10.19818/j.cnki.1671-1637.2012.03.012
Citation: ZHAO Jian-you, ZHOU Sun-feng, CUI Xiao-juan, WANG Gao-qing. Predictive method of highway freight volume based on fuzzy linear regression model[J]. Journal of Traffic and Transportation Engineering, 2012, 12(3): 80-85. doi: 10.19818/j.cnki.1671-1637.2012.03.012
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基于模糊线性回归模型的公路货运量预测方法

doi: 10.19818/j.cnki.1671-1637.2012.03.012

  • 长安大学 汽车学院, 陕西 西安 710064

基金项目: 

国家自然科学基金项目 61101216

国家自然科学基金项目 51178158

陕西省交通科技项目 07-10R

中俄国际道路运输发展研究项目 2011hj-08

详细信息
    作者简介:

    赵建有(1963-), 男, 河南西峡人, 长安大学教授, 工学博士, 从事道路交通规划研究

  • 中图分类号: U491.13

Predictive method of highway freight volume based on fuzzy linear regression model

  • School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China

More Information
    Author Bio:

    ZHAO Jian-you (1963-), male, professor, PhD, +86-29-82334371, jyzhao@chd.edu.cn

    摘要
  • 摘要: 确定了公路货运量的影响因素分别为GDP、人口数量、社会消费零售总额和农副产品产值, 构建了基于模糊线性回归模型的公路货运量预测方法。以延安市公路货运枢纽规划为实例, 1995~2004年的货运统计量作为因变量, 确定了模型的模糊系数。以2005~2010年的货运统计量作为验证值, 分析了模型的拟合精度, 并将模糊线性回归模型的预测结果与指数平滑法、灰色模型、弹性系数法3种常见预测方法的预测结果进行比较。研究结果表明: 在模糊线性回归模型中, t检验的平均值为0.673 07, 说明预测值与实际值差异不显著, 模型预测效果较好; 4种方法的平均相对误差分别为0.073 1、0.100 3、0.167 8、0.232 9, 可见, 本文方法误差最小。

     

    Abstract: The influence factors of highway freight volume were determined, such as GDP, population quantity, the total amount of social consuming retails and the output value of sideline products, and a predictive method of highway freight volume based on fuzzy linear regression model was set up.The highway freight hub planning in Yan'an City was taken as an example, the statistical freight volumes from 1995 to 2004 were taken as dependent variables, and the fuzzy coefficients of fuzzy linear regression model were determined.The statistical freight volumes from 2005 to 2010 were taken as verified values, and the goodness of fit for fuzzy linear regression model was analyzed.The predictive results among fuzzy linear regression model, exponential smoothing method, grey model and elastic coefficient method were compared.Analysis result shows that in the fuzzy linear regression model, the average value of t test is 0.673 07, which shows that the difference between predictive value and actual value is not significant, and the prediction effect is better.The average relative errors of four methods are 0.073 1, 0.100 3, 0.167 8, 0.232 9 respectively, so the error of predictive method is smallest.

     

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  • 表  1  统计数据

    Table  1.   Statistics data
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    表  2  关联度

    Table  2.   Correlation degrees
    下载: 导出CSV

    表  3  预测值与实际值

    Table  3.   Predictive values and actual values
    下载: 导出CSV

    表  4  四种方法预测值

    Table  4.   Predictive values of four methods  104t
    下载: 导出CSV

    表  5  结果比较

    Table  5.   Comparison of results
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    • 参考文献(16)
  • [1] AL-DEEK H M. Use of vessel freight data to forecast heavy truck movements at seaports[J]. Transportation Research Board, 2002 (1804): 217-224. doi: 10.3141/1804-29
    [2] GARRIDO R A, MAHMASSNI H S. Forecasting freight transportation demand with the space-time multinomial probit model[J]. Transportation Research Part B: Methodological, 2000, 34 (5): 403-418. doi: 10.1016/S0191-2615(99)00032-6
    [3] BABCOCK M W, LU Xiao-hua, NORTON J. Time series forecasting of quarterly railroad grain carloadings[J]. Trans-portation Research Part E: Logistics and Transportation Review, 1999, 35 (1): 43-57. doi: 10.1016/S1366-5545(98)00024-6
    [4] GODFREY G A, POWELL W B. An adaptive dynamic pro-gramming algorithm for dynamic fleet management, II: mul-tiperiod travel times[J]. Transportation Science, 2002, 36 (1): 40-54. doi: 10.1287/trsc.36.1.40.572
    [5] DANTAS A, YAMAMOTO K, LAMAR M V, et al. Neural network for ravel demand forecast using GIS and remote sensing[C]//University of Canterbury. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. Como: University of Canterbury, 2000: 435-440.
    [6] CHIOU H K, TZENG G H, CHENG C K, et al. Grey pre-diction model for forecasting the planning material of equip-ment spare parts in navy of Taiwan[C]//National Chiao Tung University. Proceedings of the5th World Automation Congress. Hsinchu: National Chiao Tung University, 2004: 315-320.
    [7] 杨铭, 秦华荣, 陈荫三. 区域公路货运周转量结构分析与推算方法[J]. 交通运输工程学报, 2011, 11 (5): 93-100. http://transport.chd.edu.cn/article/id/201105015

    YANG Ming, QIN Hua-rong, CHEN Yin-san. Structure analysis and calculation method of freight turnover for regional highway[J]. Journal of Traffic and Transportation Engineer-ing, 2011, 11 (5): 93-100. (in Chinese). http://transport.chd.edu.cn/article/id/201105015
    [8] 崔淑华, 王娜, 胡亚南. 基于主成分分析的公路货运量预测影响因素研究[J]. 森林工程, 2005, 21 (5): 65-67. doi: 10.3969/j.issn.1001-005X.2005.05.025

    CUI Shu-hua, WANG Na, HU Ya-nan. Influencing factors of forecasting highway fright volume based on principal com-ponents analysis[J]. Forest Engineering, 2005, 21 (5): 65-67. (in Chinese). doi: 10.3969/j.issn.1001-005X.2005.05.025
    [9] 任其亮. 灰色马尔可夫模型在公路运量弹性系数预测中的应用[J]. 重庆交通大学学报: 自然科学版, 2009, 28 (2): 290-293. https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT200902031.htm

    REN Qi-liang. Study on application of grey-Markov model to forecasting elastic coefficients in highway transportation vol-ume[J]. Journal of Chongqing Jiaotong University: Natural Science, 2009, 28 (2): 290-293. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT200902031.htm
    [10] 田智慧, 王世杰. 基于四阶段预测理论的公路交通量预测研究[J]. 郑州大学学报: 工学版, 2008, 29 (3): 133-136. doi: 10.3969/j.issn.1671-6833.2008.03.034

    TIAN Zhi-hui, WANG Shi-jie. Research on the forecast of the highway traffic volume based on the theory of the four stages forecast[J]. Journal of Zhengzhou University: Engineering Science, 2008, 29 (3): 133-136. (in Chinese). doi: 10.3969/j.issn.1671-6833.2008.03.034
    [11] 魏艳强, 刘海琳, 宁红云. 基于RBF神经网络的公路货运量预测方法研究[J]. 天津理工大学学报, 2008, 24 (1): 17-20. https://www.cnki.com.cn/Article/CJFDTOTAL-TEAR200801005.htm

    WEI Yan-qiang, LIU Hai-lin, NING Hong-yun. Measure-ment study of highway transportation volume forecast based on RBF neural network[J]. Journal of Tianjin University of Technology, 2008, 24 (1): 17-20. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TEAR200801005.htm
    [12] 周志娟, 陈森发. 组合预测方法在我国公路货运量预测中的应用[J]. 中国水运, 2010, 10 (5): 166-167. https://www.cnki.com.cn/Article/CJFDTOTAL-ZSUX201005090.htm

    ZHOU Zhi-juan, CHEN Sen-fa. Combination forecasting method of highway freight volume forecasting in our country[J]. China Water Transport, 2010, 10 (5): 166-167. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZSUX201005090.htm
    [13] 马昌喜, 郭坤卿, 马永红. 基于改进灰色-马尔可夫链方法的公路货运量预测[J]. 兰州交通大学学报, 2009, 28 (4): 124-127. https://www.cnki.com.cn/Article/CJFDTOTAL-LZTX200904034.htm

    MA Chang-xi, GUO Kun-qing, MA Yong-hong. Prediction of highway freight volumes based on improved gray-Markov chain[J]. Journal of Lanzhou Jiaotong University, 2009, 28 (4): 124-127. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-LZTX200904034.htm
    [14] FREITAS P S A, RODRIGUES A J L. Model combination in neural-based forecasting[J]. European Journal of Operational Research, 2006, 173 (3): 801-814. https://www.sciencedirect.com/science/article/pii/S0377221705006752
    [15] 曾文艺, 李洪兴, 施煜. 模糊线性回归模型(Ⅰ)[J]. 北京师范大学学报: 自然科学版, 2006, 42 (2): 120-125. https://www.cnki.com.cn/Article/CJFDTOTAL-BSDZ200602003.htm

    ZENG Wen-yi, LI Hong-xing, SHI Yu. Fuzzy linear regres-sion model (Ⅰ)[J]. Journal of Beijing Normal University: Natural Science, 2006, 42 (2): 120-125. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BSDZ200602003.htm
    [16] 曾文艺, 李洪兴, 施煜. 模糊线性回归模型(Ⅱ)[J]. 北京师范大学学报: 自然科学版, 2006, 42 (4): 334-338. https://www.cnki.com.cn/Article/CJFDTOTAL-BSDZ200602003.htm

    ZENG Wen-yi, LI Hong-xing, SHI Yu. Fuzzy linear regres-sion model (Ⅱ)[J]. Journal of Beijing Normal University: Natural Science, 2006, 42 (4): 334-338. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BSDZ200602003.htm
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  • 收稿日期:  2012-01-09
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