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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2018
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th-cmuir.6653943832-554902018-09-05T03:00:00Z Mining taxi data for describing city in the context of mobility, sociality, and environment: Lessons learned Marco Veloso Santi Phithakkitnukoon Carlos Bento Pedro D'Orey Computer Science Engineering © 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. 2018-09-05T02:57:10Z 2018-09-05T02:57:10Z 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/6653943832/55490 |
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Chiang Mai University |
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Thailand |
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Computer Science Engineering |
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Computer Science Engineering Marco Veloso Santi Phithakkitnukoon Carlos Bento Pedro D'Orey Mining taxi data for describing city in the context of mobility, sociality, and environment: Lessons learned |
| 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 |
Marco Veloso Santi Phithakkitnukoon Carlos Bento Pedro D'Orey |
| author_facet |
Marco Veloso Santi Phithakkitnukoon Carlos Bento Pedro D'Orey |
| author_sort |
Marco Veloso |
| 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 |
2018 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85010076708&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/55490 |
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1681424515511353344 |