Using App Usage Data From Mobile Devices to Improve Activity-Based Travel Demand Models

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Big Data Pub Date : 2024-02-14 DOI:10.1109/TBDATA.2024.3366088
Ana Belén Rodríguez González;Javier Burrieza-Galán;Juan José Vinagre Díaz;Inés Peirats de Castro;Mark Richard Wilby;Oliva Garcia Cantú-Ros
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Abstract

In the last years we have seen several studies showing the potential of mobile network data to reconstruct activity and mobility patterns of the population. These data sources allow continuous monitoring of the population with a higher degree of spatial and temporal resolution and at a lower cost compared with traditional methods. However, for certain applications, the spatial resolution of these data sources is still not enough since it typically provides a spatial resolution of hundreds of meters in urban areas and of few kilometers in rural areas. In this article, we fill this gap by proposing a methodology that utilises GPS data from the usage of different applications in mobile devices. This approach improves the spatial precision in the location of activities, previously identified with the mobile network data.
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利用移动设备的应用使用数据改进基于活动的旅行需求模型
在过去的几年中,我们看到了一些研究显示移动网络数据在重建人口活动和流动模式方面的潜力。与传统方法相比,这些数据源能够以更高的时空分辨率和更低的成本对人口进行连续监测。然而,在某些应用中,这些数据源的空间分辨率仍然不够,因为在城市地区,其空间分辨率通常只有几百米,而在农村地区则只有几公里。在本文中,我们提出了一种利用移动设备中不同应用的 GPS 数据的方法,从而填补了这一空白。这种方法提高了先前通过移动网络数据确定的活动位置的空间精度。
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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