Predictive model of transport needs in quito, based on the analysis of information compiled among users of the social network Twitter
DOI:
https://doi.org/10.62452/jrpscq56Keywords:
Transport, Quito municipality, Twitter, Predictive systemsAbstract
The present study proposes the retrieval of information in Big Data, a large repository of information that does not have the scope problem, that is, that data can be recovered from any part of the world, continent, country or location that is required to investigate. Its recovery speed has no comparison with manual field processing, since it depends exclusively on the speed of the hardware used. Our intention is to propose an information management model that reduces the cost of research, reduces the time required and increases the opportunity of information to improve the quality of the mobility service.
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References
Ecuador. Secretaría de Movilidad. (2020). Estrategia de Resiliencia de Quito. Secretaría de Movilidad. http://www.secretariademovilidad.quito.gob.ec/index.php/la-institucion/politica1.html
Pacheco, M. (2017). El 12% de buses no cumple parámetros de calidad en Quito. El Comercio. http://www.elcomercio.com/actualidad/buses-transportepublico-calidad-pasajeros-quito.html
Python Software Foundation. (2020). Python Enhancement Proposals: The future of Python. https://www.python.org/doc/
PowerData. (2020). Big Data: ¿En qué consiste? Su importancia, desafíos y gobernabilidad. Powerdata. https://www.powerdata.es/big-data
Ramírez Arévalo, H. H., & Herrera Cubides, J. F. (2013). Un viaje a través de bases de datos espaciales NoSQL. Udistrital. https://revistas.udistrital.edu.co/index.php/REDES/article/view/5923/7425
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Copyright (c) 2021 Tonysé de la Rosa Martín (Autor/a)
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