RT Journal Article
T1 BiPred: A Bilevel Evolutionary Algorithm for Prediction in Smart Mobility.
A1 Toutouh-el-Alamin, Jamal
A1 Arellano-Verdejo, Javier
A1 Alba-Torres, Enrique
K1 Computación evolutiva
K1 Transporte - Innovaciones tecnológicas
K1 Aprendizaje automático (Inteligencia artificial)
AB 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
PB MDPI
YR 2018
FD 2018-11-24
LK https://hdl.handle.net/10630/32558
UL https://hdl.handle.net/10630/32558
LA eng
NO Toutouh, J., Arellano, J., & Alba, E. (2018). BiPred: A bilevel evolutionary algorithm for prediction in smart mobility. Sensors, 18(12), 4123.
NO This research was partially funded by Ministerio de Economía, Industria y Competitividad, Gobierno de España, and the European Regional Development Fund, grant numbers TIN2016-81766-REDT (http://cirti.es) and TIN2017-88213-R (http://6city.lcc.uma.es). European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 799078. Universidad de Málaga. Campus Internacional de Excelencia, Andalucía TECH.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026