Urban Parking is a problem that costs time and energy. That is why intelligent parking is a field of research growing very quickly. In a city where no sensor infrastructure within each place is deployed but only a counting system at every intersection is available, we show that it still possible to propose an efficient method that determines an itinerary that minimizes the expected time to find an available parking place. For this, we first model the urban area by a graph. Then, we implement a learning algorithm that uses a reinforcement learning method. In this model, each agent modeling an intersection, learns the best next street portion. At each step, all the decisions taken by the agents generate an itinerary whose expectation time is the basis for updating the parameters of learning. The execution times and performances of the learning algorithm are compared with those of a method that constructs step by step the itinerary by choosing the next segment with an evaluation of the future expectation time within this segment. We evaluate the performance of the learning algorithm by realistic simulations. The simulation data are extracted from the map of Versailles.

This paper focuses on QoS guarantees in an interdomain selfish network where each domain may sell QoS guarantees for its transit traffic. The main objective of the paper is to evaluate the benefit for some of these domains to develop together a privileged partnership in terms of economic alliance. This alliance permits the members to share their local knowledge of the network and to exchange some traffic network services. After defining the alliance model and the way each domain may use it to obtain better QoS guarantees, we analyse by simulation on realistic generated topologies the impact of such alliances on the QoS requests satisfaction.