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: | , , , |
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| Format: | Conference Proceeding |
| Published: |
2017
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| 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 |
| Summary: | © 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. |
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