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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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dc.contributor.author Terroso-Saenz, Fernando es_ES
dc.contributor.author Muñoz-Ortega, Andrés es_ES
dc.contributor.author Cecilia-Canales, José María es_ES
dc.date.accessioned 2020-05-21T03:02:01Z
dc.date.available 2020-05-21T03:02:01Z
dc.date.issued 2019-11-08 es_ES
dc.identifier.uri http://hdl.handle.net/10251/143879
dc.description.abstract [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 has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8. es_ES
dc.description.sponsorship 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 and Universities under Grants TIN2016-78799-P (AEI/FEDER, UE) and RTC-2017-6389-5. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation MICINN/TIN2016-78799-P es_ES
dc.relation Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia /20813/PI/18 es_ES
dc.relation Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia /20530/PDC/18 es_ES
dc.relation AGENCIA ESTATAL DE INVESTIGACION/RTC-2017-6389-5-AR es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Taxi demand es_ES
dc.subject Online social networks es_ES
dc.subject Machine learning es_ES
dc.subject Air pollution es_ES
dc.subject Smart cities es_ES
dc.subject Social media analysis es_ES
dc.title QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s19224882 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation 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 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s19224882 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 22 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 19 es_ES
dc.description.issue 22 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 31717423 es_ES
dc.identifier.pmcid PMC6891530 es_ES
dc.relation.pasarela S\403851 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia es_ES


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