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Sensors 2018, 18(12), 4123; https://doi.org/10.3390/s18124123

BiPred: A Bilevel Evolutionary Algorithm for Prediction in Smart Mobility

Departamento de Lenguajes y Ciencias de la Compuitación, Universidad de Málaga, 29071 Málaga, Spain
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Received: 30 September 2018 / Revised: 17 November 2018 / Accepted: 20 November 2018 / Published: 24 November 2018
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Abstract

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. View Full-Text
Keywords: smart mobility; road-traffic prediction; dataset reduction; evolutionary algorithms; machine learning smart mobility; road-traffic prediction; dataset reduction; evolutionary algorithms; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Toutouh, J.; Arellano, J.; Alba, E. BiPred: A Bilevel Evolutionary Algorithm for Prediction in Smart Mobility. Sensors 2018, 18, 4123.

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