• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 25 Issue 1
Mar.  2012
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Article Contents
GENG Liyan, LIANG Yigang. Prediction of Railway Freight Volumes Based on Grey Adaptive Particle Swarm Least Squares Support Vector Machine Model[J]. Journal of Southwest Jiaotong University, 2012, 25(1): 144-150. doi: 10.3969/j.issn.0258-2724.2012.021.01.024
Citation: GENG Liyan, LIANG Yigang. Prediction of Railway Freight Volumes Based on Grey Adaptive Particle Swarm Least Squares Support Vector Machine Model[J]. Journal of Southwest Jiaotong University, 2012, 25(1): 144-150. doi: 10.3969/j.issn.0258-2724.2012.021.01.024

Prediction of Railway Freight Volumes Based on Grey Adaptive Particle Swarm Least Squares Support Vector Machine Model

doi: 10.3969/j.issn.0258-2724.2012.021.01.024
  • Received Date: 20 Apr 2011
  • Publish Date: 25 Feb 2012
  • In order to improve the forecasting accuracy and the modeling speed for railway freight volumes, the grey forecasting model GM (1, 1) and the adaptive particle swarm optimization (APSO) were both introduced into the least squares support vector machines (LSSVMs). Thus, a new model, the grey APSO least squares support vector machine (GM-APSO-LSSVM) model, was built. The new model weakens the stochastic factor in the original sequence and exploits the regularity of data using the grey sequence operator of the grey model in the first stage. Then, the new data are forecasted with the LSSVM featured by simple calculation, fast solving speed, and powerful non-linear mapping ability. At the same time, the parameters of LSSVM are optimized by the APSO. An empirical analysis was performed to verify the proposed model using the freight volumes data in China. The results show that the proposed model has a superior prediction performance to the existing models, and its performance indices RMSE, MAE, MPE, and Theil are 0.062 8, 0.052 3, 0.016 2, and 0.010 7, respectively, all less than those of the other models. The searching time for the optimal LSSVM parameters using the APSO is 55.656 s, which is 10.462 s less than the time spent by the conventional cross-validation method. The relative prediction errors of the model in predicting the railway freight volumes from 2006 to 2009 are 0.39%, -1.67%, 1.44% and 4.75%, respectively; therefore, the proposed model is more suitable for short-term railway freight volumes forecasting.

     

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  •  [1] 李红启,刘凯. 基于分形理论的铁路货运量分析[J]. 铁道学报,2003,25(3): 19-23.      LI Hongqi, LIU Kai. Analysis of railway freight volumes based on fractal theory[J. Journal of the China Railway Society, 2003, 25(3): 19-23.       [2] 李红启,刘凯. 基于Rough Set理论的铁路货运量预测[J]. 铁道学报,2004,26(3): 1-7.      LI Hongqi, LIU Kai. Prediction of railway freight volumes based on rough set theory[J]. Journal of the China Railway Society, 2004, 26(3): 1-7.       [3] 刘志杰,季令,叶玉玲,等. 基于径向基神经网络的铁路货运量预测[J]. 铁道学报,2006,28(5): 1-5.      LIU Zhijie, JI Ling, YE Yuling, et al. Study on prediction of railway freight volumes based on RBF neural network[J]. Journal of the China Railway Society, 2006, 28(5): 1-5.       [4] 郭玉华,陈治亚,冯芬玲,等. 基于经济周期的铁路货运量神经网络预测研究[J]. 铁道学报,2010,32(5): 1-6.      GUO Yuhua, CHEN Zhiya, FENG Fenling, et al. Railway freight volume forecasting of neural network based on economic cycles[J]. Journal of the China Railway Society, 2010, 32(5): 1-6.       [5] 周波. 基于复杂网络理论的铁路货运量预测[J]. 铁道货运,2008(3): 20-22.      ZHOU Bo. Prediction of railway freight volumes based on complex network theory[J]. Railway Freight Transport, 2008(3): 20-22.       [6] 白晓勇,郎茂祥. 铁路货运量预测的改进BP神经网络方法[J]. 交通运输系统工程与信息,2006,6(6): 158-162.      BAI Xiaoyong, LANG Maoxiang. An improved BP neural network in the railway freight volume forecast[J.] Journal of Transportation Systems Engineering and Information Technology, 2006, 6(6): 158-162.       [7] 张诚,周湘峰. 基于灰色预测-马尔可夫链定性分析的铁路货运量预测[J]. 铁道学报,2007,29(5): 15-21.      ZHANG Cheng, ZHOU Xiangfeng. Prediction of railway freight volumes based on gray forecast-Markov chain qualitative analysis[J]. Journal of the China Railway Society, 2007, 29(5): 15-21.       [8] 陈维荣,郑永康,戴朝华,等. 基于复Morlet小波SVM的负荷预测[J]. 西南交通大学学报,2009,44(5): 631-636.      CHEN Weirong, ZHENG Yongkang, DAI Chaohua, et al. Short-term load forecasting based on complex Morlet wavelet SVM[J]. Journal of Southwest Jiaotong University, 2009, 44(5): 631-636.       [9] 赵闯,刘凯,李电生. 支持向量机在货运量预测中的应用研究[J]. 铁道学报,2004,26(4): 10-14.      ZHAO Chuang, LIU Kai, LI Diansheng. Research on application of support vector machine in freight volumes forecast[J]. Journal of the China Railway Society, 2004, 26(4): 10-14.       [10] 王治. 基于遗传算法-支持向量机的铁路货运量预测[J]. 计算机仿真,2010,27(12): 320-322.     WANG Zhi. Prediction of railway freight volume based on genetic algorithm-support vector machine[J]. Computer Simulation, 2010, 27(12): 320-322.      [11] SUYKENS J A K, VANDEVALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293-300.      [12] 刘陆洲,肖建,王嵩. 基于在线LS-SVM的阶逆控制[J]. 西南交通大学学报,2009,44(3): 375-379.     LIU Luzhou, XIAO Jian, WANG Song. th-order inverse control based on online least square support vector machines[J]. Journal of Southwest Jiaotong University, 2009, 44(3): 375-379.      [13] SHI Y, EBERHART R. A modified particle swarm optimizer[C]∥Proceedings of IEEE International Conference on Evolutionary Computation. Anchorage: [s. n., 1998: 69-73.      [14] 刘思峰,谢乃明. 灰色系统理论及其应用[M]. 4版. 北京:科学出版社,2008: 8-9.      [15] 陈维荣,关佩,邹月娴. 基于SVM的交通事件检测技术[J]. 西南交通大学学报,2011,46(1): 63-67.     CHEN Weirong, GUAN Pei, ZOU Yuexian. Automatic incident detection technology based on SVM[J]. Journal of Southwest Jiaotong University, 2011, 46(1): 63-67.      [16] 吉培荣,黄巍松,胡翔勇. 无偏灰色预测模型[J]. 系统工程与电子技术,2000,22(6): 6-7.     JI Peirong, HUANG Weisong, HU Xiangyong. An unbiased grey forecasting model[J]. Systems Engineering and Electronics, 2000, 22(6): 6-7.      [17] 张大海,江世芳,史开泉. 灰色预测公式的理论缺陷及改进[J]. 系统工程理论与实践,2002,22(8): 1-3.     ZHANG Dahai, JIANG Shifang, SHI Kaiquan. Theoretical defect of grey prediction formula and its improvement[J]. Systems Engineering: Theory and Practice, 2002, 22(8): 1-3.
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