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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2018  >  18(3): 210-217
LEI Ding-you, MA Qiang, XU Xin-ping, LIU Qing-yi. Forecasting method of expressway traffic volume based on NPCA and GA-RBF[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 210-217. doi: 10.19818/j.cnki.1671-1637.2018.03.021
Citation: LEI Ding-you, MA Qiang, XU Xin-ping, LIU Qing-yi. Forecasting method of expressway traffic volume based on NPCA and GA-RBF[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 210-217. doi: 10.19818/j.cnki.1671-1637.2018.03.021
shu

Forecasting method of expressway traffic volume based on NPCA and GA-RBF

doi: 10.19818/j.cnki.1671-1637.2018.03.021

  • School of Traffic and Transportation Engineering, Central South University, Changsha 410075, Hunan, China

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  • Author Bio:

    LEI Ding-you(1958-), male, professor, PhD, ding@csu.edu.cn

    XU Xin-ping(1991-), male, graduate student, xuxinping@csu.edu.cn

  • Received Date: 2017-12-12
  • Publish Date: 2018-06-25
    Abstract
  • To improve expressway traffic volume forecasting accuracy, on the basis of the features of expressway traffic volume which was obviously nonlinear and affected by many factors, a forecasting method based on the nonlinear principal component analysis and GA-optimized RBF neural network (NPCA-GA-RBF) method was presented. The main influencing index of expressway traffic volume was determined, and the nonlinear principal component analysis method was used to reduce the dimensionality and correlation of influencing factors. The original multiple indexes were replaced by a few principal components to simplify the structure of the neural network. The GA was used to optimize the parameters of the RBF neural network and further improve the forecasting accuracy of traffic volume. An expressway in Puer City was taken as the example to verify the traffic volume forecasting method. Analysis result shows that theaverage relative errors of the GA-RBF and NPCA-GA-RBF methods in two experiments are 1.62%, 3.53% and 2.27%, 3.32% smaller than that of the RBF model, respectively, so the GA-optimized RBF neural network improves the traffic volume forecasting accuracy of the RBF method. Compared to the GA-RBF method, the average relative error of the NPCA-GA-RBF method decreases by 1.91% and 1.05% in two experiments, respectively, its traffic volume forecasting result is closer to the actual traffic volume and more reliable, which demonstrates that the nonlinear principal component analysis eliminates the correlation of the indexes to further improve the traffic volume forecasting accuracy and reduces the forecasting complexity of traffic volume. So, the NPCA-GA-RBF method has higher traffic volume forecasting accuracy, can provide the reliable decision basis for expressway operations management, and satisfies the objective requirements of the reasonable management for expressway.

     

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    • References(27)
  • [1]
    肖润谋, 李彬, 陈荫三. 基于大数据的高速公路运输趋势分析[J]. 交通运输工程学报, 2015, 15 (5): 85-90, 117. doi: 10.3969/j.issn.1671-1637.2015.05.011

    XIAO Run-mou, LI Bin, CHEN Yin-san. Trend analysis of expressway transportation based on big data[J]. Journal of Traffic and Transportation Engineering, 2015, 15 (5): 85-90, 117. (in Chinese). doi: 10.3969/j.issn.1671-1637.2015.05.011
    [2]
    刘帅, 唐伯明, 陈坚. 区域综合客运通道多方式竞争力博弈模型[J]. 交通运输工程学报, 2017, 17 (2): 136-142. doi: 10.3969/j.issn.1671-1637.2017.02.015

    LIU Shuai, TANG Bo-ming, CHEN Jian. Multi-mode competitiveness game model in regional comprehensive passenger transportation corridor[J]. Journal of Traffic and Transportation Engineering, 2017, 17 (2): 136-142. (in Chinese). doi: 10.3969/j.issn.1671-1637.2017.02.015
    [3]
    章锡俏, 王守恒, 孟祥海. 基于经济增长的高速公路诱增交通量预测[J]. 哈尔滨工业大学学报, 2007, 39 (10): 1618-1620. doi: 10.3321/j.issn:0367-6234.2007.10.025

    ZHANG Xi-qiao, WANG Shou-heng, MENG Xiang-hai. Study of freeway induced traffic volume forecasting based on economic growth[J]. Journal of Harbin Institute of Technology, 2007, 39 (10): 1618-1620. (in Chinese). doi: 10.3321/j.issn:0367-6234.2007.10.025
    [4]
    张通, 张骏, 杨霄. 基于混合AGO-SVM的高速公路短时交通量预测研究[J]. 交通运输系统工程与信息, 2011, 11 (1): 157-162. doi: 10.3969/j.issn.1009-6744.2011.01.027

    ZHANG Tong, ZHANG Jun, YANG Xiao. Short-term highway traffic flow prediction based on mixed AGO-SVM[J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11 (1): 157-162. (in Chinese). doi: 10.3969/j.issn.1009-6744.2011.01.027
    [5]
    林文新, 王建伟, 袁长伟. 高速公路交通量预测的GM (1, 1) 残差改进模型[J]. 长安大学学报: 自然科学版, 2011, 31 (5): 77-79, 96. https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL201105015.htm

    LIN Wen-xin, WANG Jian-wei, YUAN Chang-wei. Residuals improved GM (1, 1) model of expressway traffic volume prediction[J]. Journal of Chang'an University: Natural Science Edition, 2011, 31 (5): 77-79, 96. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL201105015.htm
    [6]
    桂滨, 周伟. 基于运输需求函数的区域高速公路网交通量预测模型[J]. 公路, 2012 (1): 136-138. https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL201201031.htm

    GUI Bin, ZHOU Wei. Traffic volume prediction model of regional expressway network based on transport demand function[J]. Highway, 2012 (1): 136-138. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL201201031.htm
    [7]
    钱超, 许宏科, 徐娜, 等. 基于OLAM的高速公路交通量多维预测研究[J]. 交通运输系统工程与信息, 2013, 13 (2): 48-56. doi: 10.3969/j.issn.1009-6744.2013.02.008

    QIAN Chao, XU Hong-ke, XU Na, et al. OLAM-based multi-dimensional prediction of expressway traffic volume[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13 (2): 48-56. (in Chinese). doi: 10.3969/j.issn.1009-6744.2013.02.008
    [8]
    王慧勇, 晏秋. 基于灰色线性回归组合模型的高速公路交通量预测[J]. 交通运输工程与信息学报, 2016, 14 (1): 53-57. doi: 10.3969/j.issn.1672-4747.2016.01.009

    WANG Hui-yong, YAN Qiu. Forecast of expressway traffic volume based on the grey linear regression combined model[J]. Journal of Transportation Engineering and Information, 2016, 14 (1): 53-57. (in Chinese). doi: 10.3969/j.issn.1672-4747.2016.01.009
    [9]
    丁志坤, 朱梦炼, 宋义勇. 基于改进"四阶段法"的高速公路交通量预测研究[J]. 重庆交通大学学报: 自然科学版, 2017, 36 (5): 86-90. doi: 10.3969/j.issn.1674-0696.2017.05.15

    DING Zhi-kun, ZHU Meng-lian, SONG Yi-yong. Traffic forecast of highway based on improved"four-stage method"[J]. Journal of Chongqing Jiaotong University: Natural Science, 2017, 36 (5): 86-90. (in Chinese). doi: 10.3969/j.issn.1674-0696.2017.05.15
    [10]
    JIA Zhu-zheng, CAO Jin-xin, ZHU Yuan. Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections[J]. Transportation Research Part C: Emerging Technologies, 2014, 47 (2): 139-154.
    [11]
    HUANG Xiao-lin, MAIER A, HORNEGGER J, et al. Indefinite kernels in least squares support vector machines and principal component analysis[J]. Applied and Computational Harmonic Analysis, 2017, 43 (1): 162-172. doi: 10.1016/j.acha.2016.09.001
    [12]
    MORETTI F, PIZZUTI S, PANZIERI S, et al. Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling[J]. Neurocomputing, 2015, 167: 3-7. doi: 10.1016/j.neucom.2014.08.100
    [13]
    TATAR A, NASERI S, SIRACH N, et al. Prediction of reservoir brine properties using radial basis function (RBF) neural network[J]. Petroleum, 2015, 1 (4): 349-357. doi: 10.1016/j.petlm.2015.10.011
    [14]
    ABDI J, MOSHIRI B, ABDULHAI B, et al. Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm[J]. Engineering Applications of Artificial Intelligence, 2012, 25 (5): 1022-1042.
    [15]
    王炎, 王华. 基于BP-NN和遗传算法的高速公路交通量预测[J]. 计算机工程与应用, 2006, 42 (4): 226-228. doi: 10.3321/j.issn:1002-8331.2006.04.070

    WANG Yan, WANG Hua. Prediction of highway traffic based on BP-neural network and genetic algorithms[J]. Computer Engineering and Applications, 2006, 42 (4): 226-228. (in Chinese). doi: 10.3321/j.issn:1002-8331.2006.04.070
    [16]
    王锡伟, 高军伟, 张彬, 等. 改进粒子群算法优化RBF的交通流预测研究[J]. 青岛大学学报: 工程技术版, 2015, 30 (4): 43-46. https://www.cnki.com.cn/Article/CJFDTOTAL-QDDX201504010.htm

    WANG Xi-wei, GAO Jun-wei, ZHANG Bin, et al. Traffic forecasting application based on improved particle swarm optimization and RBF neural Network[J]. Journal of Qingdao University: Engineering and Technology Edition, 2015, 30 (4): 43-46. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-QDDX201504010.htm
    [17]
    YANG Hong-jun, HU Xu. Wavelet neural network with improved genetic algorithm for traffic flow time series prediction[J]. Optik, 2016, 127 (19): 8103-8110.
    [18]
    黄文明, 徐双双, 邓珍容, 等. 改进人工蜂群算法优化RBF神经网络的短时交通流预测[J]. 计算机工程与科学, 2016, 38 (4): 713-719. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJK201604016.htm

    HUANG Wen-ming, XU Shuang-shuang, DENG Zhen-rong, et al. Short-term traffic flow prediction of optimized RBF neural networks based on the modified ABC algorithm[J]. Computer Engineering and Science, 2016, 38 (4): 713-719. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJK201604016.htm
    [19]
    HE Hong-di, WANG Jun-li, WEI Hai-rui, et al. Fractal behavior of traffic volume on urban expressway through adaptive fractal analysis[J]. Physica A: Statistical Mechanics and Its Applications, 2016, 443: 518-525.
    [20]
    MIRGOLBABAEI H, ECHEKKI T, SMAOUI N. A nonlinear principal component analysis approach for turbulent combustion composition space[J]. International Journal of Hydrogen Energy, 2014, 39 (9): 4622-4633.
    [21]
    刘兴彬, 万发祥. RBF神经网络主成分分析法在交通量预测中应用[J]. 山西科技, 2007 (1): 54-56. https://www.cnki.com.cn/Article/CJFDTOTAL-SXKJ200701024.htm

    LIU Xing-bin, WANG Fa-xiang. Application of RBF main neural network analytic approach in traffic volume prediction[J]. Shanxi Science and Technology, 2007 (1): 54-56. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-SXKJ200701024.htm
    [22]
    姚智胜, 邵春福, 熊志华, 等. 基于主成分分析和支持向量机的道路网短时交通流量预测[J]. 吉林大学学报: 工学版, 2008, 38 (1): 48-52. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY200801010.htm

    YAO Zhi-sheng, SHAO Chun-fu, XIONG Zhi-hua, et al. Short-term traffic volumes forecasting of road network based on principal component analysis and support vector machine[J]. Journal of Jilin University: Engineering and Technology Edition, 2008, 38 (1): 48-52. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY200801010.htm
    [23]
    肖智, 李玲玲. PSO-SVM在高速公路交通量预测中的应用[J]. 管理评论, 2011, 23 (12): 32-37, 67. https://www.cnki.com.cn/Article/CJFDTOTAL-ZWGD201112005.htm

    XIAO Zhi, LI Ling-ling. Forecast of highway traffic volume using PSO-SVM[J]. Management Review, 2011, 23 (12): 32-37, 67. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZWGD201112005.htm
    [24]
    孙超平, 杨善林, 施敏加. 基于非线性主成分分析法的SWOT战略定位模型研究[J]. 合肥工业大学学报: 自然科学版, 2012, 35 (12): 1702-1708. https://www.cnki.com.cn/Article/CJFDTOTAL-HEFE201212027.htm

    SUN Chao-ping, YANG Shan-lin, SHI Min-jia. Research on SWOT strategic model based on non-linear principal component analysis[J]. Journal of Hefei University of Technology: Natural Science Edition, 2012, 35 (12): 1702-1708. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HEFE201212027.htm
    [25]
    KAGODA P A, NDIRITU J, NTULI C, et al. Application of radial basis function neural networks to short-term streamflow forecasting[J]. Physics and Chemistry of the Earth, 2010, 35 (13/14): 571-581.
    [26]
    AIBINU A M, SALAU H B, RAHMAN N A, et al. A novel clustering based genetic algorithm for route optimization[J]. Engineering Science and Technology, 2016, 19 (4): 2022-2034.
    [27]
    ZHAO Ze-hui, KANG Hai-gui, LI Ming-wei. Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model[J]. Journal of Southeast University: English Edition, 2013, 29 (3): 328-335.
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