Human Mobility from theory to practice:Data, Models and Applications
Human Mobility from theory to practice:Data, Models and Applications
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DOI：
10.1145/3308560.3320099
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Luca Pappalardo;Gianni Barlacchi;Roberto Pellungrini;F. Simini
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文献类型：
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作者：
Luca Pappalardo;Gianni Barlacchi;Roberto Pellungrini;F. Simini
个人设备中的跟踪技术为分析GPS跟踪和通话详细记录等大量移动数据打开了大门。本教程概述了人类移动性的建模原则和适用于特定问题的机器学习模型。我们回顾了人类移动性的五个主要方面的最新技术水平：（1）人类移动性数据景观;（2）个人和集体移动性的关键措施;（3）个人，群体和两者混合的生成模型;（4）下一个位置预测算法;（5）社会公益应用。对于每个方面，我们使用本教程的演示者开发的Python库“scikit-mobility”进行实验和模拟。
The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library ”scikit-mobility” developed by the presenters of the tutorial.