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Information metrics for improved traffic model fidelity through sensitivity analysis and data assimilation

Author

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  • Sopasakis, A.
  • Katsoulakis, M.A.

Abstract

We develop theoretical and computational tools which can appraise traffic flow models and optimize their performance against current time-series traffic data and prevailing conditions. The proposed methodology perturbs the parameter space and undertakes path-wise analysis of the resulting time series. Most importantly the approach is valid even under non-equilibrium conditions and is based on procuring path-space (time-series) information. More generally we propose a mathematical methodology which quantifies traffic information loss.

Suggested Citation

  • Sopasakis, A. & Katsoulakis, M.A., 2016. "Information metrics for improved traffic model fidelity through sensitivity analysis and data assimilation," Transportation Research Part B: Methodological, Elsevier, vol. 86(C), pages 1-18.
    • Handle: RePEc:eee:transb:v:86:y:2016:i:c:p:1-18
    DOI: 10.1016/j.trb.2016.01.003
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    Cited by:

    1. Zhang, Canrong & Guan, Hao & Yuan, Yifei & Chen, Weiwei & Wu, Tao, 2020. "Machine learning-driven algorithms for the container relocation problem," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 102-131.

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