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Traffic state estimation based on Eulerian and Lagrangian observations in a mesoscopic modeling framework

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  • Duret, Aurélien
  • Yuan, Yufei

Abstract

The paper proposes a model-based framework for estimating traffic states from Eulerian (loop) and/or Lagrangian (probe) data. Lagrangian-Space formulation of the LWR model adopted as the underlying traffic model provides suitable properties for receiving both Eulerian and Lagrangian external information. Three independent methods are proposed to address Eulerian data, Lagrangian data and the combination of both, respectively. These methods are defined in a consistent framework so as to be implemented simultaneously. The proposed framework has been verified on the synthetic data derived from the same underlying traffic flow model. Strength and weakness of both data sources are discussed. Next, the proposed framework has been applied to a freeway corridor. The validity has been tested using the data from a microscopic simulator, and the performance is satisfactory even for low rate of probe vehicles around 5%.

Suggested Citation

  • Duret, Aurélien & Yuan, Yufei, 2017. "Traffic state estimation based on Eulerian and Lagrangian observations in a mesoscopic modeling framework," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 51-71.
    • Handle: RePEc:eee:transb:v:101:y:2017:i:c:p:51-71
    DOI: 10.1016/j.trb.2017.02.008
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    1. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part III: Multi-destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 305-313, August.
    2. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part II: Queueing at freeway bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 289-303, August.
    3. Herrera, Juan C. & Bayen, Alexandre M., 2010. "Incorporation of Lagrangian measurements in freeway traffic state estimation," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 460-481, May.
    4. Paul I. Richards, 1956. "Shock Waves on the Highway," Operations Research, INFORMS, vol. 4(1), pages 42-51, February.
    5. Wang, Yibing & Papageorgiou, Markos, 2005. "Real-time freeway traffic state estimation based on extended Kalman filter: a general approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(2), pages 141-167, February.
    6. Deng, Wen & Lei, Hao & Zhou, Xuesong, 2013. "Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 132-157.
    7. Laval, Jorge A. & Leclercq, Ludovic, 2013. "The Hamilton–Jacobi partial differential equation and the three representations of traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 52(C), pages 17-30.
    8. Daganzo, Carlos F., 2005. "A variational formulation of kinematic waves: basic theory and complex boundary conditions," Transportation Research Part B: Methodological, Elsevier, vol. 39(2), pages 187-196, February.
    9. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part I: General theory," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 281-287, August.
    10. Ngoduy, D., 2008. "Applicable filtering framework for online multiclass freeway network estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 599-616.
    11. Shane Velan & Michael Florian, 2002. "A Note on the Entropy Solutions of the Hydrodynamic Model of Traffic Flow," Transportation Science, INFORMS, vol. 36(4), pages 435-446, November.
    12. Ansorge, Rainer, 1990. "What does the entropy condition mean in traffic flow theory?," Transportation Research Part B: Methodological, Elsevier, vol. 24(2), pages 133-143, April.
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    Cited by:

    1. Zheng, Fangfang & Jabari, Saif Eddin & Liu, Henry X. & Lin, DianChao, 2018. "Traffic state estimation using stochastic Lagrangian dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 143-165.
    2. Bekiaris-Liberis, Nikolaos & Roncoli, Claudio & Papageorgiou, Markos, 2017. "Highway traffic state estimation per lane in the presence of connected vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 1-28.
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    4. Wang, Zhengli & Qi, Xin & Jiang, Hai, 2018. "Estimating the spatiotemporal impact of traffic incidents: An integer programming approach consistent with the propagation of shockwaves," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 356-369.

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