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

Abstract

© 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.