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Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian theory

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Abstract

Considering the chaotic characteristics of traffic flow, this study proposes a Bayesian theory-based multiple measures chaotic time series prediction algorithm. In particular, a time series of three traffic measures, i.e., speed, occupancy, and flow, obtained from different sources is used to reconstruct the phase space using the phase space reconstruction theory. Then, data from the multiple sources are combined using Bayesian estimation theory to identify the chaotic characteristics of traffic flow. In addition, a radial basis function (RBF) neural network is designed to predict the traffic flow. Compared to the consideration of a single source, results from numerical experiments demonstrate the improved effectiveness of the proposed multi-measure method in terms of accuracy and timeliness for the short-term traffic flow prediction.

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Acknowledgments

We acknowledge the support from various grant sources: the National Natural Science Foundation of China (Grant No. 61304197), the Scientific and Technological Talents of Chongqing (Grant No. cstc2014kjrc-qnrc30002), the Key Project of Application and Development of Chongqing (Grant No. cstc2014yykfB40001), Wenfeng Talents of Chongqing University of Posts and Telecommunications,“151” Science and Technology Major Project of Chongqing—General Design and Innovative Capability of Full Information Based Traffic Guidance and Control System (Grant No. cstc2013jcsf-zdzxqqX0003)—the Doctoral Start-up Funds of Chongqing University of Posts and Telecommunication (Grant No. A2012-26), and the US Department of Transportation through the NEXTRANS Center, the USDOT Region 5 University Transportation Center.

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Authors and Affiliations

  1. Chongqing Collaborative Innovation Center for Information Communication Technology, College of Automation and Center for Automotive Electronics and Embedded System, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Yongfu Li, Xiao Jiang, Hao Zhu, Taixiong Zheng & Yinguo Li

  2. School of Civil Engineering and The NEXTRANS Center, Purdue University, West Lafayette, IN, 47906, USA

    Yongfu Li, Xiaozheng He & Srinivas Peeta

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  1. Yongfu Li
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  2. Xiao Jiang
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  3. Hao Zhu
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  4. Xiaozheng He
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  5. Srinivas Peeta
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  6. Taixiong Zheng
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  7. Yinguo Li
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Correspondence to Yongfu Li.

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Li, Y., Jiang, X., Zhu, H. et al. Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian theory. Nonlinear Dyn 85, 179–194 (2016). https://doi.org/10.1007/s11071-016-2677-5

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  • DOI: https://doi.org/10.1007/s11071-016-2677-5

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