Volume 4 Issue 4
. 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2004  >  4(4): 68-71
SHI Qi-xin, ZHENG Wei-zhong. Short-term traffic flow prediction methods comparison of road networks[J]. Journal of Traffic and Transportation Engineering, 2004, 4(4): 68-71.
Citation: SHI Qi-xin, ZHENG Wei-zhong. Short-term traffic flow prediction methods comparison of road networks[J]. Journal of Traffic and Transportation Engineering, 2004, 4(4): 68-71.
shu

Short-term traffic flow prediction methods comparison of road networks

  • Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China

More Information
  • Author Bio:

    SHI Qi-xin(1946-), male, professor, 86-10-62784496, dcisqx@tsinghua.edu.cn

  • Received Date: 2004-06-07
  • Publish Date: 2004-12-25
    Abstract
  • A large number of techniques have been applied into short-term traffic flow prediction, which can be classified into two groups: statistical models and artificial neural network model. The models and their application were discussed and compared. Several models, including historical average, ARIMA (auto regressive integrated moving average) model, nonparametric regression, RBF (radial basis function) neural network and Bayesian combined neural network model were applied into a numerical example of short-term traffic volume prediction in a field network, their prediction results and performances were compared. It was found that the error of hybrid neural network model is littlest, its prediction reliability is highest, it is the most effective method to predicte short-term traffic flow.

     

    • FullText(HTML)
  • loading
    • References(17)
  • [1]
    Ben-Akiva M, Koutsopoulos H N, Mukundan A. A dynamic traffic model system for ATMS/ATIS operations[J]. IVHS Journal, 1994, 2 (1): 1-19.
    [2]
    CheslowM, Hatcher S G, Patel VM. An initial evaluation of alternative intelligent vehicle highway systems architecture[R]. MITRE Report 92w0000063, MITRE Corporation, 1992.
    [3]
    Davis G A, Nihan N L. Nonparametric regression and short term freeway traffic forecasting[J]. Journal of Transportation Engineering, 1991, 117(2): 178-188. doi: 10.1061/(ASCE)0733-947X(1991)117:2(178)
    [4]
    BoxG E P, JenkinsGM. Time series analysis: forecasting and control [R]. San Francisco: Holden-Day, 1977.
    [5]
    Kalman R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering, 1960, 82(1): 35-45. doi: 10.1115/1.3662552
    [6]
    Okutani I, Stephanedes Y J. Dynamic prediction of traffic volume through Kalman filtering theory[J]. Transportation Research, Part B, 1984, 18(1): 1-11. doi: 10.1016/0191-2615(84)90002-X
    [7]
    Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression[J]. The American Statistician, 1992, 46(3): 175-185.
    [8]
    Dougherty MS. A review of neural networks applied to transport[J]. Transportation Research, Part C, 1995, 3(4): 247-260.
    [9]
    Zhang H J, Ritchie S G, Lo Z P. Macroscopic modeling of freeway traffic using an artificial neural network[J]. Transportation Research Record, 1997, 1588: 110-119. doi: 10.3141/1588-14
    [10]
    Faghri A, Hua J. Evaluation of artificial neural network applications in transportation engineering[J]. Transportation Research Record, 1992, 1358: 71-80.
    [11]
    Dougherty M S, Kirby H C. The use of neural networks to recognize and predict traffic congestion[J]. Traffic Engineering and Control, 1993, 34(6): 311-314.
    [12]
    Park B, Messer C J, Urbanik T. Shortterm freeway traffic volume forecasting using radial basis function neural network[J]. Transportation Research Record, 1998, 1651: 39-47. doi: 10.3141/1651-06
    [13]
    Abdulhai B, Porwal H, Recker W. Short-term freeway traffic flow prediction using genetically-optimized time-delay-based neural networks [R]. Institute of Transportation Studies, University of California, 1998.
    [14]
    Dia H. An object-oriented neural network approach to short-term traffic forecasting[J]. European Journal of Operational Research, 2001, 131(2): 253-261. doi: 10.1016/S0377-2217(00)00125-9
    [15]
    Van D V, Dougherty M, Watson S. Combining kohonen maps with ARIMA time series models to forecast traffic flow[J]. Transportation Research, Part C, 1996, 4(5): 307-318.
    [16]
    Park D, Rilett L R. Forecasting multiple-period freeway link travel times using modular neural networks[J]. Transportation Research Record, 1998, 1617: 163-170. doi: 10.3141/1617-23
    [17]
    Lee D, Zheng W, Shi Q. Short-term freeway traffic flow prediction using a combined neural network model[A]. The 83rd Annual Meeting of TRB[C]. Washington D C: TRB, 2004.
    • 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 (247) PDF downloads(784) 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