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Dynamic Data Driven Particle Filter for Agent-Based Traffic State Estimation

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Cloud Computing and Security (ICCCS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9483))

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Abstract

Particle filter is a good algorithm to deal with non-linear and non-Gaussian problem, but it undergoes high computational complexity and particle degradation problem, and there are few researches in the field of agent-based traffic state estimation. In this paper, a dynamic data driven particle filter is proposed to estimate the traffic states by assimilating real-time data from limited sensors. As the simulation run, the proposed particle filter method can optimize its execution strategies based on the simulation result; furthermore, the real-time data injected into the method can be adjusted dynamically. The agent-based simulation model can display the results in detail, and the traffic state on all roads can be estimated when the particle filter execute to certain precision. Experimental results indicate the framework can estimate the traffic state effectively and the improved particle filter algorithm poses a high accuracy with faster speed.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Xiang-wen Feng & Xue-feng Yan

  2. Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA

    Xiao-lin Hu

Authors
  1. Xiang-wen Feng
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  2. Xue-feng Yan
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  3. Xiao-lin Hu
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Corresponding author

Correspondence to Xiang-wen Feng .

Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and As, Nanjing, China

    Zhiqiu Huang

  2. Nanjing University of Information Scienc, Nanjing, China

    Xingming Sun

  3. Nanjing, China

    Junzhou Luo

  4. Nanjing University of Aeronautics and As, Nanjing, China

    Jian Wang

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© 2015 Springer International Publishing Switzerland

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Feng, Xw., Yan, Xf., Hu, Xl. (2015). Dynamic Data Driven Particle Filter for Agent-Based Traffic State Estimation. In: Huang, Z., Sun, X., Luo, J., Wang, J. (eds) Cloud Computing and Security. ICCCS 2015. Lecture Notes in Computer Science(), vol 9483. Springer, Cham. https://doi.org/10.1007/978-3-319-27051-7_27

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  • DOI: https://doi.org/10.1007/978-3-319-27051-7_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27050-0

  • Online ISBN: 978-3-319-27051-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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