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In recent years, with the growing popularity of smartphone, near field communication (NFC) based mobile
applications commenced to be used in public transportation. This development provides an opportunity to collect additional and province independent data about passengers and so it allows development of better data mining applications. The present study is conducted to compare classification algorithms on public transport data collected by NFC-based mobile phone ticketing application for the first time. In this paper, five popular classification algorithms have been considered to investigate various target attributes in terms of accuracy rates: Naive Bayes,
C4.5 Decision Tree, Random Forest, Support Vector Machines, and k-Nearest Neighbor. The study presented in this paper can be useful to provide decision support for public transportation.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Ufuk Demir Alan
D. BİRANT