Towards Smart Mobility: Journey Reconstruction for Frictionless Public Transit using GPS and GTFS Data

This paper delivers a state-of-the-art survey on frictionless travel for on-the-road public transportation using Global Position System (GPS) data. Different Automatic Fare Collection (AFC) approaches and the importance of journey reconstruction in public transit for accurate route and fare generation and, consequently, route and fare correction are explained. A journey reconstruction engine using the passenger's GPS and real-time General Transit Feed Specification (GTFS) data from the city public transit API is developed for the Check-In/Check-Out (CICO) ticketing approach. In addition, an intuitive GPS fetching time selection is explained to decrease battery drain while using the proposed architecture. Furthermore, different scenarios for collecting data in Hamilton, ON, Canada, are defined to study the effectiveness of the proposed system. To validate the model, data was collected using our developed transportation application, which enables travellers to check in and check out upon boarding and disembarking the bus. The routes were compared using real- time GTFS data to determine the passenger's transit services and accurately reconstruct their journeys. The performance of the journey reconstruction engine was assessed across multiple scenarios, with future directions for the research also explored.