Mining Taxi Data for Describing City in the Context of Mobility, Sociality, and Environment: Lessons Learned

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Veloso, M (Author) Other; Phithakkitnukoon, S (Author) Other; Bento, C (Author) Other; Pedro M. d'Orey (Author) FEUP

Conference proceedings International

Title: IEEE 19th International Conference on Intelligent Transportation Systems: Proceedings

Pages: 217-222

ITSC 2016 - 19th IEEE International Conference on Intelligent Transportation Systems

Rio de Janeiro, 1 de novembro de 2016

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Other information

Authenticus ID: P-00M-CQB

DOI: 10.1109/itsc.2016.7795557

Abstract (EN): 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 naive 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6