Lagrangian generic second order traffic flow models for node - Archive ouverte HAL
Article Dans Une Revue Journal of Traffic and Transportation Engineering Année : 2018

Lagrangian generic second order traffic flow models for node

Résumé

This study sheds light on higher order macroscopic traffic flow modeling on road networks, thanks to the generic second order models (GSOM family) which embeds a myriad of traffic models. It has been demonstrated that such higher order models are easily solved in Lagrangian coordinates which are compatible with both microscopic and macroscopic descriptions. The generalized GSOM model is reformulated in the Lagrangian coordinate system to develop a more efficient numerical method. The difficulty in applying this approach on networks basically resides in dealing with node dynamics. Traffic flow characteristics at node are different from that on homogeneous links. Different geometry features can lead to different critical research issues. For instance, discontinuity in traffic stream can be an important issue for traffic signal operations, while capacity drop may be crucial for lane-merges. The current paper aims to establish and analyze a new adapted node model for macroscopic traffic flow models by applying upstream and downstream boundary conditions on the Lagrangian coordinates in order to perform simulations on networks of roads, and accompanying numerical method. The internal node dynamics between upstream and downstream links are taken into account of the node model. Therefore, a numerical example is provided to underscore the efficiency of this approach. Simulations show that the discretized node model yields accurate results. Additional kinematic waves and contact discontinuities are induced by the variation of the driver attribute.

Dates et versions

hal-03544937 , version 1 (27-01-2022)

Identifiants

Citer

Asma Khelifi, Jean Patrick Lebacque, Habib Haj-Salem, Lotfi Nabli. Lagrangian generic second order traffic flow models for node. Journal of Traffic and Transportation Engineering, 2018, 5 (1), pp.14-27. ⟨10.1016/j.jtte.2017.08.001⟩. ⟨hal-03544937⟩
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