Volume 12 Issue 2
. 
Turn off MathJax
Article Contents
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
shu

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

More Information
  • 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.

     

    • FullText(HTML)
  • loading
    • 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.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1065) PDF downloads(813) Cited by()
    Proportional views
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return