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QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

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QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

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Terroso-Saenz, F.; Muñoz-Ortega, A.; Cecilia-Canales, JM. (2019). QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis. Sensors. 19(22):1-22. https://doi.org/10.3390/s19224882

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Título: QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis
Autor: Terroso-Saenz, Fernando Muñoz-Ortega, Andrés Cecilia-Canales, José María
Entidad UPV: Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Fecha difusión:
Resumen:
[EN] Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort ...[+]
Palabras clave: Taxi demand , Online social networks , Machine learning , Air pollution , Smart cities , Social media analysis
Derechos de uso: Reconocimiento (by)
Fuente:
Sensors. (eissn: 1424-8220 )
DOI: 10.3390/s19224882
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/s19224882
Código del Proyecto:
info:eu-repo/grantAgreement/MINECO//TIN2016-78799-P/ES/DESARROLLO HOLISTICO DE APLICACIONES EMERGENTES EN SISTEMAS HETEROGENEOS/
info:eu-repo/grantAgreement/f SéNeCa//20813%2FPI%2F18/
info:eu-repo/grantAgreement/f SéNeCa//20530%2FPDC%2F18/
info:eu-repo/grantAgreement/AEI//RTC-2017-6389-5/ES/PLANIFICACIÓN Y GESTIÓN DE RECURSOS HÍDRICOS A PARTIR DE ANÁLISIS DE DATOS DE IoT (WATERoT)/
Agradecimientos:
This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Projects 20813/PI/18 and 20530/PDC/18 and by the Spanish Ministry of Science, Innovation ...[+]
Tipo: Artículo

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