Posts tagged "smart city"

Traffic simulation at Toulouse city scale

Context Presentation

The ANR SWITCH project is focused on studying the impact of smart city new transport modes on infrastructure. Although the new transports (electric, autonomous vehicles, transport on demand, bicycles, etc.) can facilitate intra-urban mobility and improve the quality of life, they can also create new constraints for which cities must be prepared. Then urban planners need tools to evaluate the impact of urban policies in terms of mobility and infrastructure to explore “What if? " scenarios.

These new simulation tools require mobility simulations on a city scale and on different time scales. As part of the meta-model design for the Smart City simulation, this internship is about developing an urban mobility model allowing to couple a flow-based circulation model with a multi-agent model allowing fine modeling of driver behavior. The internship must carry out a proof of concept on the Gama platform of this type of system on the scale of an agglomeration.

poster - loic sadou


Traffic Simulation, GAMA, Multi-Agent Simulation, Smart City, Transport

Scientific goal

•    To validate a mesoscopic traffic multi-agent model considering various transports facilities


Opportunistic Software Composition

Context Presentation

Cyber-physical and ambient systems surround the human user with services at her/his disposal. From these services, complex composites services, tailored to the user preferences and the current situation, can be composed automatically and on the fly.

To produce the knowledge necessary for automatic composition in the absence of both prior expression of the user's needs and specification of a process or a composition model, we develop a generic solution based on online reinforcement learning. It is decentralized within a multi-agent system in charge of the administration and composition of the services, which learns incrementally from and for the user.


diagram_WY - walid YOUNES


Ambient intelligence; Service Discovery, Selection and Composition; Multi-agent System; Machine learning; Smart city; neOCampus

 Scientific goals

•    Design a decentralized and distributed system that learns and decides on compositions.

•    Consider user preferences and context.


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