A Lightweight Approach for Building User Mobility Profiles
Bibliographic Data
| ID | 22033504 |
|---|---|
| Authors | Sebastián Vallejos (0000-0001-5375-4373, Consejo Nacional de Investigaciones Científicas y Técnicas), Luis Berdun (0000-0002-8006-8691, Consejo Nacional de Investigaciones Científicas y Técnicas), Marcelo Armentano (0000-0002-9172-9061, Consejo Nacional de Investigaciones Científicas y Técnicas), Silvia Schiaffino (0000-0002-7616-9756, Consejo Nacional de Investigaciones Científicas y Técnicas), Daniela Godoy (0000-0001-7673-7154, Consejo Nacional de Investigaciones Científicas y Técnicas, corresponding author) |
| Year | 2023 |
| Volume | 13 |
| Issue | 1 |
| Pages | 11 |
| Publication date | 2023-12-27 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi13010011 |
| OpenAlex | W4390275095 |
| Language | EN |
| References cited | 48 |
Data captured by mobile devices enable us, among other things, learn the places where users go, identify their home and workplace, the places they usually visit (e.g., supermarket, gym, etc.), the different paths they take to move from one place to another and even their routines. In summary, with this information, it is possible to learn a user mobility profile. In this work, we propose a lightweight approach for building mobility profiles from data collected with mobile devices. The mobility profiles of a user consist of the places visited, the visit history and the travel paths. Our approach aims to solve some of the challenges and limitations identified in the literature. Particularly, it considers geographic information to identify certain kinds of places, such as open spaces, big places and small places, that are hard to distinguish with existing approaches. We use different sensors and time frequencies to collect data in order to optimize battery consumption and maximize precision. Finally, it executes entirely on the mobile devices, avoiding the exposure of sensitive user information and then preserving user privacy. The proposal was evaluated in the context of the real usage of the developed prototype applications in two cities of Argentina. The results obtained with our approach outperformed other approaches in the literature, both in precision and recall
Data science · Geography · Human–computer interaction · Mobile device · Mobility model · Telecommunications · World Wide Web · Computer Science · Context-Aware Activity Recognition Systems · Human Mobility and Location-Based Analysis · Mobile Crowdsensing and Crowdsourcing
| Citation velocity | historical |
|---|---|
| Highly cited | No |