Mining taxi data for describing city in the context of mobility, sociality, and environment: Lessons learned

© 2016 IEEE. Taxi is an important way of transportation. With the equipped location sensors, it becomes a probe sensing urban dynamics. In this work, we review and improve three approaches that use taxi data to explore the city dynamics of Lisbon, Portugal. We develop a naïve Bayesian classifier to...

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Main Authors:	Veloso M., Phithakkitnukoon S., Bento C., D'Orey P.
Format:	Conference Proceeding
Published:	2017
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85010076708&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/41197
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-41197
record_format	dspace
spelling	th-cmuir.6653943832-411972017-09-28T04:19:53Z Mining taxi data for describing city in the context of mobility, sociality, and environment: Lessons learned Veloso M. Phithakkitnukoon S. Bento C. D'Orey P. © 2016 IEEE. Taxi is an important way of transportation. With the equipped location sensors, it becomes a probe sensing urban dynamics. In this work, we review and improve three approaches that use taxi data to explore the city dynamics of Lisbon, Portugal. We develop a naïve Bayesian classifier to estimate taxi demand; analyze the correlation between taxi volume and mobile phone activity; and compare ANN and linear regression models to estimate NO2 concentrations, using taxi activity information and meteorological conditions. 2017-09-28T04:19:53Z 2017-09-28T04:19:53Z 2016-12-22 Conference Proceeding 2-s2.0-85010076708 10.1109/ITSC.2016.7795557 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85010076708&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/41197
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
description	© 2016 IEEE. Taxi is an important way of transportation. With the equipped location sensors, it becomes a probe sensing urban dynamics. In this work, we review and improve three approaches that use taxi data to explore the city dynamics of Lisbon, Portugal. We develop a naïve Bayesian classifier to estimate taxi demand; analyze the correlation between taxi volume and mobile phone activity; and compare ANN and linear regression models to estimate NO2 concentrations, using taxi activity information and meteorological conditions.
format	Conference Proceeding
author	Veloso M. Phithakkitnukoon S. Bento C. D'Orey P.
spellingShingle	Veloso M. Phithakkitnukoon S. Bento C. D'Orey P. Mining taxi data for describing city in the context of mobility, sociality, and environment: Lessons learned
author_facet	Veloso M. Phithakkitnukoon S. Bento C. D'Orey P.
author_sort	Veloso M.
title	Mining taxi data for describing city in the context of mobility, sociality, and environment: Lessons learned
title_short	Mining taxi data for describing city in the context of mobility, sociality, and environment: Lessons learned
title_full	Mining taxi data for describing city in the context of mobility, sociality, and environment: Lessons learned
title_fullStr	Mining taxi data for describing city in the context of mobility, sociality, and environment: Lessons learned
title_full_unstemmed	Mining taxi data for describing city in the context of mobility, sociality, and environment: Lessons learned
title_sort	mining taxi data for describing city in the context of mobility, sociality, and environment: lessons learned
publishDate	2017
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85010076708&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/41197
_version_	1681421957688459264

Mining taxi data for describing city in the context of mobility, sociality, and environment: Lessons learned

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