Public transportation ticketing has evolved from traditional paper tickets to advanced digital systems. This study combines GPS data from users’ smartphones with General Transit Feed Specification (GTFS) data from the bus network in Hamilton, Ontario, to analyze trajectory similarities using Dynamic Time Warping (DTW) and Longest Common Subsequence (LCSS) algorithms. By matching user trajectories with GTFS data, the system accurately identifies the bus services used, enabling frictionless fare calculation and integration of payment systems. Our results show that DTW is more effective than LCSS, particularly for longer trips due to the large quantity of data points. This research demonstrates the practicality of this approach, providing a promising solution for improving fare collection and the efficiency of public transportation. These findings make a significant contribution to the development of smart, user-friendly transportation infrastructure. / Thesis / Master of Applied Science (MASc)