2011
DOI: 10.1051/m2an/2010105
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On the modelling and management of traffic

Abstract: Abstract. Several realistic situations in vehicular traffic that give rise to queues can be modeled through conservation laws with boundary and unilateral constraints on the flux. This paper provides a rigorous analytical framework for these descriptions, comprising stability with respect to the initial data, to the boundary inflow and to the constraint. We present a framework to rigorously state optimal management problems and prove the existence of the corresponding optimal controls. Specific cases are dealt… Show more

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Cited by 49 publications
(50 citation statements)
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“…Another extension of this work is to consider the ADR model with constraints that are non-local in time. Such constraints allow to tackle optimal management problems in the spirit of [27,28].…”
Section: Discussionmentioning
confidence: 99%
“…Another extension of this work is to consider the ADR model with constraints that are non-local in time. Such constraints allow to tackle optimal management problems in the spirit of [27,28].…”
Section: Discussionmentioning
confidence: 99%
“…It is obtained by coupling the Aw-Rascle-Zhang (ARZ) model, see [4,21], with a fixed local point constraint on the (density) flux. Conservation laws with unilateral constraints on the flux were first introduced in [11] and then studied in [1,3,8,9] in the case of first order traffic flow models (consisting in scalar conservation laws) to describe the situations in which "obstacles" like toll gates, traffic lights or construction sites are present, see [10,19]. We recall that in this case the presence of the constraint may enforce the appearance of undercompressive stationary jump discontinuities, which do not satisfy the Kruzhkov entropy condition [16] even though the first Rankine-Hugoniot condition remains valid, ensuring mass conservation.…”
Section: Introductionmentioning
confidence: 99%
“…We recall that the concept of point constraints was introduced in the framework of vehicular traffic in [19] and in the framework of crowd dynamics in [26]. We defer to [1,2,3,4,5,6,16,17,21,22,23,28,29,40,50] for further developments and applications also to crowd dynamics.…”
Section: Introductionmentioning
confidence: 99%