Lagrangian generic second order traffic flow models for node

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 com...

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Main Authors: Asma Khelifi, Habib Haj-Salem, Jean-Patrick Lebacque, Lotfi Nabli
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2018-02-01
Series:Journal of Traffic and Transportation Engineering (English ed. Online)
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2095756416302422
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spelling doaj-2982c834309d498194ab64516ffcf5e22021-03-02T11:03:32ZengKeAi Communications Co., Ltd.Journal of Traffic and Transportation Engineering (English ed. Online)2095-75642018-02-0151142710.1016/j.jtte.2017.08.001Lagrangian generic second order traffic flow models for nodeAsma Khelifi0Habib Haj-Salem1Jean-Patrick Lebacque2Lotfi Nabli3Engineering of Surface Transportation Networks and Advanced Computing Laboratory, IFSTTAR/GRETTIA, Marne la Vallée 77447, FranceEngineering of Surface Transportation Networks and Advanced Computing Laboratory, IFSTTAR/GRETTIA, Marne la Vallée 77447, FranceEngineering of Surface Transportation Networks and Advanced Computing Laboratory, IFSTTAR/GRETTIA, Marne la Vallée 77447, FranceDepartment of Electrical Engineering, Research Laboratory of Control, Signal Processing and Imaging, National Engineering School of Monastir, Monastir 5000, TunisiaThis 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.http://www.sciencedirect.com/science/article/pii/S2095756416302422MacroscopicTraffic flow modelingGSOM familyLagrangianNode
collection DOAJ
language English
format Article
sources DOAJ
author Asma Khelifi
Habib Haj-Salem
Jean-Patrick Lebacque
Lotfi Nabli
spellingShingle Asma Khelifi
Habib Haj-Salem
Jean-Patrick Lebacque
Lotfi Nabli
Lagrangian generic second order traffic flow models for node
Journal of Traffic and Transportation Engineering (English ed. Online)
Macroscopic
Traffic flow modeling
GSOM family
Lagrangian
Node
author_facet Asma Khelifi
Habib Haj-Salem
Jean-Patrick Lebacque
Lotfi Nabli
author_sort Asma Khelifi
title Lagrangian generic second order traffic flow models for node
title_short Lagrangian generic second order traffic flow models for node
title_full Lagrangian generic second order traffic flow models for node
title_fullStr Lagrangian generic second order traffic flow models for node
title_full_unstemmed Lagrangian generic second order traffic flow models for node
title_sort lagrangian generic second order traffic flow models for node
publisher KeAi Communications Co., Ltd.
series Journal of Traffic and Transportation Engineering (English ed. Online)
issn 2095-7564
publishDate 2018-02-01
description 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.
topic Macroscopic
Traffic flow modeling
GSOM family
Lagrangian
Node
url http://www.sciencedirect.com/science/article/pii/S2095756416302422
work_keys_str_mv AT asmakhelifi lagrangiangenericsecondordertrafficflowmodelsfornode
AT habibhajsalem lagrangiangenericsecondordertrafficflowmodelsfornode
AT jeanpatricklebacque lagrangiangenericsecondordertrafficflowmodelsfornode
AT lotfinabli lagrangiangenericsecondordertrafficflowmodelsfornode
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