Traffic State Estimation Using Stochastic Lagrangian Dynamics

This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates. The source of uncertainty is heterogeneity in driving behavior, captured using driver-specific speed-spacing relations, i.e., parametric uncertainty. It also results in smooth vehicle trajectories in a stochastic context, which is in agreement with real-world traffic dynamics and, thereby, overcoming issues with aggressive oscillation typically observed in sample paths of stochastic traffic flow models. The authors utilize ensemble filtering techniques for data assimilation (traffic state estimation), but derive the mean and covariance dynamics as the ensemble sizes go to infinity, thereby bypassing the need to sample from the parameter distributions while estimating the traffic states. As a result, the estimation algorithm is just a standard Kalman-Bucy algorithm, which renders the proposed approach amenable to real-time applications using recursive data. Data assimilation examples are performed and the results indicate good agreement with out-of-sample data.

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  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    C2SMART Connected Cities with Smart Transportation

    NYU Tandon School of Engineering
    Department of Civil and Urban Engineering
    Brooklyn, NY  United States 

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
    • Zheng, Fangfang
    • Jabari, Saif Eddin
    • Liu, Henry X
    • Lin, DianChao
  • Publication Date: 2018


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 34p

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Filing Info

  • Accession Number: 01708259
  • Record Type: Publication
  • Contract Numbers: NSFC 61673321
  • Created Date: Jun 3 2019 8:32AM