This article develops the design, installation, exploitation, and final utilization of intelligent techniques, hardware, and software for understanding mobility in a modern city. We focus on a smart-campus initiative in the University of Malaga as the scenario for building this cyber⁻physical system at a low cost, and then present the details of a new proposed evolutionary algorithm used for better training machine-learning techniques: BiPred. We model and solve the task of reducing the size of the dataset used for learning about campus mobility. Our conclusions show an important reduction of the required data to learn mobility patterns by more than 90%, while improving (at the same time) the precision of the predictions of theapplied machine-learning method (up to 15%). All this was done along with the construction of a real system in a city, which hopefully resulted in a very comprehensive work in smart cities using sensors.
Keywords: dataset reduction; evolutionary algorithms; machine learning; road-traffic prediction; smart mobility.