A learning algorithm to minimize the expectation time of finding a parking place in urban area

摘要

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.