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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2012  >  12(2): 105-111
YU Bin, WU Shan-hua, WANG Ming-hua, ZHAO Zhi-hong. K-nearest neighbor model of short-term traffic flow forecast[J]. Journal of Traffic and Transportation Engineering, 2012, 12(2): 105-111. doi: 10.19818/j.cnki.1671-1637.2012.02.015
Citation: YU Bin, WU Shan-hua, WANG Ming-hua, ZHAO Zhi-hong. K-nearest neighbor model of short-term traffic flow forecast[J]. Journal of Traffic and Transportation Engineering, 2012, 12(2): 105-111. doi: 10.19818/j.cnki.1671-1637.2012.02.015
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K-nearest neighbor model of short-term traffic flow forecast

doi: 10.19818/j.cnki.1671-1637.2012.02.015
  • 1.

    School of Transportation Management, Dalian Maritime University, Dalian 116026, Liaoning, China

  • 2.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

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  • Author Bio:

    YU Bin (1977-), male, associate professor, PhD, +86-411-84726756, yubinyb@163.com

  • Received Date: 2011-10-29
  • Publish Date: 2012-04-25
    Abstract
  • In order to accurately forecast the short-term traffic flow, a K-nearest neighbor(K-NN) model was set up.The time and space parameters of the K-NN model were analyzed.Based on four different combinations of state vectors, the time dimension model, upstream section-time dimension model, downstream section-time dimension model and space-time dimension model were proposed.The four different models were validated by using the GPS data from taxis of Guiyang.Analysis result indicates that the K-NN model with both space and time parameters has highest forecasting precision than the other three models, and its average prediction error is about 7.26%.The distance measuring mode with exponent weight has higher accuracy in choosing the nearest neighbors, and its average prediction error is about 5.57%.The predicting performance of improved K-NN model with exponent weight and space-time parameters is best compared with the artificial neural network model and the historical average model, and its average prediction error is only 9.43%.So the improved K-NN model is an effective way for forecasting short-term traffic flow.

     

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    • References(16)
  • [1]
    张晓利, 陆化普. 非参数回归方法在短时交通流预测中的应用[J]. 清华大学学报: 自然科学版, 2009, 49(9): 1471-1475. doi: 10.3321/j.issn:1000-0054.2009.09.011

    ZHANG Xiao-li, LU Hua-pu. Non-parametric regression and application for short-term traffic flow forecasting[J]. Journal of Tsinghua University: Science and Technology, 2009, 49(9): 1471-1475. (in Chinese). doi: 10.3321/j.issn:1000-0054.2009.09.011
    [2]
    刘静, 关伟. 交通流预测方法综述[J]. 公路交通科技, 2004, 21(3): 82-85. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK2020S1025.htm

    LIU Jing, GUAN Wei. A summary of traffic flow forecasting methods[J]. Journal of Highway and Transportation Research and Development, 2004, 21(3): 82-85. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK2020S1025.htm
    [3]
    朱顺应, 王红, 李关寿. 路段上短时间区段内交通量预测ARIMA模型[J]. 重庆交通学院学报, 2003, 22(1): 76-77, 95. https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT200301019.htm

    ZHU Shun-ying, WANG Hong, LI Guan-shou. The ARIMA model used in forecasting of traffic volume in short interval on the link[J]. Journal of Chongqing Jiaotong University, 2003, 22(1): 76-77, 95. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT200301019.htm
    [4]
    OKUTANI I, STEPHANEDES Y J. Dynamic prediction of traffic volume through Kalman filtering theory[J]. Transportation Research Part B: Methodological, 1984, 18(l): 1-11.
    [5]
    聂佩林, 余志, 何兆成. 基于约束卡尔曼滤波的短时交通流量组合预测模型[J]. 交通运输工程学报, 2008, 8(5): 86-90. http://transport.chd.edu.cn/article/id/200805017

    NIE Pei-lin, YU Zhi, HE Zhao-cheng. Constrained Kalman filter combined predictor for short-term traffic flow[J]. Journal of Traffic and Transportation Engineering, 2008, 8(5): 86-90. (in Chinese). http://transport.chd.edu.cn/article/id/200805017
    [6]
    宋国杰, 胡程, 谢昆青, 等. 面向实时短时交通流预测的过程神经元网络建模[J]. 交通运输工程学报, 2009, 9(5): 73-77. http://transport.chd.edu.cn/article/id/200905013

    SONG Guo-jie, HU Cheng, XIE Kun-qing, et al. Process neural network modeling for real time short-term traffic flow prediction[J]. Journal of Traffic and Transportation Engineering, 2009, 9(5): 73-77. (in Chinese). http://transport.chd.edu.cn/article/id/200905013
    [7]
    SMITH B L, WILLIAMS B M, OSWALD R K. Comparison of parametric and nonparametric models for traffic flow forecasting[J]. Transportation Research Part C: Emerging Technologies, 2002, 10(4): 303-321. doi: 10.1016/S0968-090X(02)00009-8
    [8]
    HSU C W, LIN C J. A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425. doi: 10.1109/72.991427
    [9]
    徐启华, 杨瑞. 支持向量机在交通流量实时预测中的应用[J]. 公路交通科技, 2005, 22(12): 131-134. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200512033.htm

    XU Qi-hua, YANG Rui. Traffic flow prediction using sup-port vector machine based method[J]. Journal of Highway and Transportation Research and Development, 2005, 22(12): 131-134. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200512033.htm
    [10]
    干宏程, 汪晴, 范炳全. 基于宏观交通流模型的行程时间预测[J]. 上海理工大学学报, 2008, 30(5): 409-413. https://www.cnki.com.cn/Article/CJFDTOTAL-HDGY200805002.htm

    GAN Hong-cheng, WANG Qing, FAN Bing-quan. Travel time prediction based on macroscopic traffic flow models[J]. Journal of University of Shanghai for Science and Technology, 2008, 30(5): 409-413. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HDGY200805002.htm
    [11]
    翁剑成, 荣健, 任福田, 等. 基于非参数回归的快速路行程速度短期预测算法[J]. 公路交通科技, 2007, 24(3): 93-97, 106. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200703021.htm

    WENG Jian-cheng, RONG Jian, REN Fu-tian, et al. Non-parametric regression model based short-term prediction for expressway travel speed[J]. Journal of Highway and Trans-portation Research and Development, 2007, 24(3): 93-97, 106. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200703021.htm
    [12]
    张晓利, 贺国光, 陆化普. 基于K-邻域非参数回归短时交通流预测方法[J]. 系统工程学报, 2009, 24(2): 178-183. https://www.cnki.com.cn/Article/CJFDTOTAL-XTGC200902009.htm

    ZHANG Xiao-li, HE Guo-guang, LU Hua-pu. Short-term traffic flow forecasting based on K-nearest neighbors non-parametric regression[J]. Journal of Systems Engineering, 2009, 24(2): 178-183. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTGC200902009.htm
    [13]
    DAVIS G A, NIHAN N L. Nonparametric regression and short-term freeway traffic forecasting[J]. Journal of Trans-portation Engineering, 1991, 117(2): 178-188.
    [14]
    张涛, 陈先, 谢美萍, 等. 基于K近邻非参数回归的短时交通流预测方法[J]. 系统工程理论与实践, 2010, 30(2): 376-384. https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201002026.htm

    ZHANG Tao, CHEN Xian, XIE Mei-ping, et al. K-NN based nonparametric regression method for short-term traffic flow forecasting[J]. Systems Engineering—Theory and Practice, 2010, 30(2): 376-384. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201002026.htm
    [15]
    SMITH B L, DEMETSKY M J. Traffic flow forecasting: comparison of modeling approaches[J]. Journal of Transpor-tation Engineering, 1997, 123(4): 261-266.
    [16]
    YU Bin, LAM W H K, TAM M L. Bus arrival time predic-tion at bus stop with multiple routes[J]. Transportation Research Part C: Emerging Technologies, 2011, 19(6): 1157-1170.
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