Abstract
This paper presents a generic data assimilation framework based on a mesoscopic-LWR model formulated in Lagrangian-space coordinates and using Lagrangian observations. This is a challenging work since probe trajectories are not directly related to specific vehicle/platoon indexes in the simulation model. Therefore, we develop a method to incorporate probe information and to further estimate states. The proposed method has been validated on a homogeneous road stretch, and it provides promising results for further extension of the framework.
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References
Courant, R., Friedrichs, K., Levy, H.: On the partial difference equations of mathematical physics. IBM J. Res. Dev. 11(2), 215–234 (1967)
Duret, A., Leclercq, L., El Faouzi, N.E.: Data assimilation based on a mesoscopic-LWR modeling framework and loop detector data: methodology and application on a large-scale network. In: Proceedings of the Transportation Research Board 95th Annual meeting. Transportation Research Board, Washington, D.C. (2016)
Laval, J., Leclercq, L.: The Hamilton–Jacobi partial differential equation and the three representations of traffic flow. Transp. Res. Part B Methodol. 52, 17–30 (2013)
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© 2016 Springer International Publishing Switzerland
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Yuan, Y., Duret, A., van Lint, H. (2016). Network-Wide Mesoscopic State Estimation Based on a Variational Formulation of the LWR Model and Using Lagrangian Observations. In: Knoop, V., Daamen, W. (eds) Traffic and Granular Flow '15. Springer, Cham. https://doi.org/10.1007/978-3-319-33482-0_70
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DOI: https://doi.org/10.1007/978-3-319-33482-0_70
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-33481-3
Online ISBN: 978-3-319-33482-0
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