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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2013  >  13(2): 114-119
XU Yan-yan, ZHAI Xi, KONG Qing-jie, LIU Yun-cai. Short-term prediction method of freeway traffic flow[J]. Journal of Traffic and Transportation Engineering, 2013, 13(2): 114-119. doi: 10.19818/j.cnki.1671-1637.2013.02.017
Citation: XU Yan-yan, ZHAI Xi, KONG Qing-jie, LIU Yun-cai. Short-term prediction method of freeway traffic flow[J]. Journal of Traffic and Transportation Engineering, 2013, 13(2): 114-119. doi: 10.19818/j.cnki.1671-1637.2013.02.017
shu

Short-term prediction method of freeway traffic flow

doi: 10.19818/j.cnki.1671-1637.2013.02.017
  • 1.

    Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai Jiaotong University, Shanghai 200240, China

  • 2.

    Transportation Information Center, Shanghai Urban-Rural Construction and Transportation Development Research Institute, Shanghai 200032, China

More Information
  • Author Bio:

    XU Yan-yan(1987-), male, doctoral student, +86-21-34204028, xustone1987@gmail.com

    LIU Yurcai(1948-), male, professor, PhD, +86-21-34204340, whomliu@sjtu.edu.cn

  • Received Date: 2012-11-09
  • Publish Date: 2013-04-25
    Abstract
  • According to the complexity and nonlinearity characteristics of short-term traffic flow, the application of classification and regression tree model in freeway traffic volume prediction was investigated, and its including growing, splitting and pruning of the model was studied.The real traffic volume data of the freeways in Portland State of US was tested and verified.Afterwards, the experimental result of model was compared with the traditional ARIMA model and Kalman filtering model by using the error analysis methods of RMSE and MAPE.Comparison result indicates that the RMSEs of tree model are 42.1% and 13.1% lower than ARIMA model and Kalman filtering model, respectively.

     

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    • References(17)
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