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. M.
Mining Taxi Data for Describing City in the Context of Mobility, Sociality, and Environment: Lessons Learned, Proc IEEE Conf. on Intelligent Transportation Systems, Rio de Janeiro, Brazil, Vol. 1, pp. 217 - 222, November, 2016.
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Abstract
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.
