|Table of Contents|

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

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2012年03期
Page:
80-85
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Predictive method of highway freight volume based on fuzzy linear regression model
Author(s):
ZHAO Jian-you ZHOU Sun-feng CUI Xiao-juan WANG Gao-qing
School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China
Keywords:
traffic planning highway freight fuzzy linear regression predictive method grodness of fit influence factor
PACS:
U491.13
DOI:
-
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 from2005 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. 5 tabs, 16 refs.

References:

[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.
[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.
[3] BABCOCK M W, LU Xiao-hua, NORTON J. Time series forecasting of quarterly railroad grain carloadings[J]. Transportation Research Part E: Logistics and Transportation Review, 1999, 35(1): 43-57.
[4] GODFREY G A, POWELL W B. An adaptive dynamic programming algorithm for dynamic fleet management, II: multiperiod travel times[J]. Transportation Science, 2002, 36(1): 40-54.
[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 prediction model for forecasting the planning material of equipment spare parts in navy of Taiwan[C]//National Chiao Tung University. Proceedings of the 5th 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. 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 Engineering, 2011, 11(5): 93-100.(in Chinese)
[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.CUI Shu-hua, WANG Na, HU Ya-nan. Influencing factors of forecasting highway fright volume based on principal components analysis[J]. Forest Engineering, 2005, 21(5): 65-67.(in Chinese)
[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. REN Qi-liang. Study on application of grey-Markov model to forecasting elastic coefficients in highway transportation volume[J]. Journal of Chongqing Jiaotong University: Natural Science, 2009, 28(2): 290-293.(in Chinese)
[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. 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)
[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. WEI Yan-qiang, LIU Hai-lin, NING Hong-yun. Measurement 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)
[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. 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)
[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. 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)
[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.
[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. ZENG Wen-yi, LI Hong-xing, SHI Yu. Fuzzy linear regression model(Ⅰ)[J]. Journal of Beijing Normal University: Natural Science, 2006, 42(2): 120-125.(in Chinese)
[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. ZENG Wen-yi, LI Hong-xing, SHI Yu. Fuzzy linear regression model(Ⅱ)[J]. Journal of Beijing Normal University: Natural Science, 2006, 42(4): 334-338.(in Chinese)

Memo

Memo:
-
Last Update: 2012-06-30