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Short-term traffic flow prediction for multi traffic states on urban expressway network

Dong Chun-Jiao Shao Chun-Fu Zhuge Cheng-Xiang

Short-term traffic flow prediction for multi traffic states on urban expressway network

Dong Chun-Jiao, Shao Chun-Fu, Zhuge Cheng-Xiang
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  • Abstract views:  1288
  • PDF Downloads:  486
  • Cited By: 0
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  • Received Date:  18 February 2011
  • Accepted Date:  13 April 2011
  • Published Online:  05 January 2012

Short-term traffic flow prediction for multi traffic states on urban expressway network

  • 1. Key Laboratory for Urban Transportation Complex Systems Theory and Technology of Ministry of Education,linebreak Beijing Jiaotong University, Beijing 100044, China;
  • 2. Center for Transportation Research, University of Tennessee, Tennessee 37996, USA
Fund Project:  Project supported by the State Key Development Program for Basic Research of China (Grant No. 2006CB705500), the National Natural Science Foundation of China (Grant No. 51178032), Doctoral Thesis Fund, Research and Development Foundation for Chinese Young Professionals, the General Motors Company, and Foundation for Excellent Doctoral Student of the Science and Technology Innovation, Beijing Jiaotong University, China (Grant No. 141082522).

Abstract: Short-term traffic flow prediction for multi traffic states on urban expressway network is carried out in this paper. The model for multi traffic states is proposed by integrating the spatial and the temporal distribution characteristics of traffic flow parameters under free traffic, congested traffic and jam traffic respectively. Based on the classical traffic flow conservation equation, the ideology of spatial and temporal dispersions in partial differential equations is adopted to establish short-time traffic flow prediction model. Meanwhile, the impact factors, such as on and off ramp, lane change and road slope are considered, which convertes short-term traffic flow prediction model into short-time traffic flow prediction state space model. Finally, the short-term traffic flow prediction for multi traffic states on urban expressway network is realized. The empirical research shows that compared with the classic ARMA model, the proposed method can not only realize short-term traffic flow prediction for multi traffic states on urban expressway network but also achieve an accuracy of 90.23%. In the same condition, the accuracy of ARMA model is 81%.

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