Human Mobility Prediction With Region-Based Flows and Water Consumption
| Autor: | Julio Fernandez-Pedauye, José M. Cecilia, Andrés Muñoz, Fernando Terroso-Saenz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Information privacy
Human mobility forecasting methods General Computer Science Computer science Context (language use) Task (project management) law.invention Bluetooth Water consumption law General Materials Science geography geography.geographical_feature_category Location data business.industry location data General Engineering Data science Residential area TK1-9971 ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES Information and Communications Technology Forecasting methods Facilitator Global Positioning System Electrical engineering. Electronics. Nuclear engineering business water consumption |
| Zdroj: | IEEE Access, Vol 9, Pp 88651-88663 (2021) RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| ISSN: | 2169-3536 |
| Popis: | [EN] We are witnessing an increasing need to accurately measure people's mobility as it has become an instrumental factor for the development of innovative services in multiple domains. In this context, several ICT solutions have relied on location-based technologies such as GPS, WiFi or Bluetooth to track individual's movements. However, these technologies are limited by the privacy restrictions of data providers. In this paper we propose a methodology to robustly predict citizens' mobility patterns based on heterogeneous data from different sources. Particularly, our methodology focuses on a human mobility predictor based on a low-resolution mobility dataset and the use of water consumption data as a facilitator of this prediction task. As a result, this work explores whether the water consumption within a geographical region can reveal human activity patterns relevant from the point of view of the mobility mining discipline. This approach has been tested in a residential area near Madrid (Spain) obtaining quite promising results. This work was supported in part by the Spanish Ministry of Science and Innovation, through the Ramon y Cajal Program under Grant RYC2018-025580-I, Grant RTI2018-096384-B-I00, and Grant RTC-2017-6389-5; in part by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and in part by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020," Spain, under Grant AICO/2020/302. |
| Databáze: | OpenAIRE |
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