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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Bibliographic Details
Main Authors: Marco Veloso, Santi Phithakkitnukoon, Carlos Bento, Pedro D'Orey
Format: Conference Proceeding
Published: 2018
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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/6653943832/55490
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Institution: Chiang Mai University
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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.