Section: Application Domains
Vehicular and pedestrian traffic flows
Intelligent Transportation Systems (ITS) is nowadays a booming sector, where the contribution of mathematical modeling and optimization is widely recognized. In this perspective, traffic flow models are a commonly cited example of "complex systems", in which individual behavior and selforganization phenomena must be taken into account to obtain a realistic description of the observed macroscopic dynamics [103] . Further improvements require more advanced models, keeping into better account interactions at the microscopic scale, and adapted control techniques, see [57] and references therein. In particular, we will focus on the following aspects:

Junction models. We are interested in designing a general junction model both satisfying basic analytical properties guaranteeing wellposedness and being realistic for traffic applications. In particular, the model should be able to overcome severe drawbacks of existing models, such as restrictions on the number of involved roads and prescribed split ratios [70] , [96] , which limit their applicability to real world situations. HamiltonJacobi equations could be also an interesting direction of research, following the recent results obtained in [107] .

Data assimilation. In traffic flow modeling, the capability of correctly estimating and predicting the state of the system depends on the availability of rich and accurate data on the network. Up to now, the most classical sensors are fixed ones. They are composed of inductive loops (electrical wires) that are installed at different spatial positions of the network and that can measure the traffic flow, the occupancy rate (i.e. the proportion of time during which a vehicle is detected to be over the loop) and the speed (in case of a system of two distant loops). These data are useful / essential to calibrate the phenomenological relationship between flow and density which is known in the traffic literature as the Fundamental Diagram. Nowadays, thanks to the wide development of mobile internet and geolocalization techniques and its increasing adoption by the road users, smartphones have turned into perfect mobile sensors in many domains, including in traffic flow management. They can provide the research community with a large database of individual trajectory sets that are known as Floating Car Data (FCD), see [105] for a real field experiment. Classical macroscopic models, say (hyperbolic systems of) conservation laws, are not designed to take into account this new kind of microscopic data. Other formulations, like HamiltonJacobi partial differential equations, are most suited and have been intensively studied in the past five years (see [64] , [65] ), with a stress on the (fixed) Eulerian framework. Up to our knowledge, they have not been studied in the timeLagrangian as well as spaceLagrangian frameworks, where data coming from mobile sensors could be easily assimilated, due to the fact that the Lagrangian coordinate (say the label of a vehicle) is fixed.

Control of autonomous vehicles. Traffic flow is usually controlled via traffic lights or variable speed limits, which have fixed space locations. The deployment of autonomous vehicles opens new perspectives in traffic management, as the use of a small fraction of cars to optimize the overall traffic. In this perspective, the possibility to track vehicles trajectories either by coupled micromacro models [78] , [97] or via the HamiltonJacobi approach [64] , [65] could allow to optimize the flow by controlling some specific vehicles corresponding to internal conditions.