Departamento de Lenguajes y Ciencias de la Compuitación, Universidad de Málaga, 29071 Málaga, Spain.
Sensors (Basel). 2018 Nov 24;18(12):4123. doi: 10.3390/s18124123.
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.
本文开发了用于理解现代城市中移动性的智能技术、硬件和软件的设计、安装、开发和最终利用。我们专注于马拉加大学的智能校园计划，以此作为以低成本构建这种网络物理系统的场景，然后介绍一种新提出的进化算法 BiPred 的详细信息，该算法用于更好地训练机器学习技术。我们对减少用于学习校园移动性的数据集大小的任务进行建模和求解。我们的结论表明，通过使用超过 90%的数据来学习移动模式，同时提高（同时）应用机器学习方法（高达 15%）的预测精度。所有这些都是在城市中构建实际系统的同时完成的，希望这能为使用传感器的智慧城市带来非常全面的工作。
