Quantifying transit travel experiences from the users’ perspective with high-resolution smartphone and vehicle location data: Methodologies, validation, and example analyses

•A system for high-resolution tracking of travelers’ transit trips is presented.•Detailed smartphone and vehicle GPS data with no additional information are matched.•In- and out-of-vehicle portions of trips are identified and compared to the schedule.•Validation shows 93% accuracy, and example wait/transfer time analyses are presented.•The output of the system enables numerous possible applications and future research.

While transit agencies have increasingly adopted systems for collecting data on passengers and vehicles, the ability to derive high-resolution passenger trajectories and directly associate them with transit vehicles in a general and transferable manner remains a challenge. In this paper, a system of integrated methods is presented to reconstruct and track travelers usage of transit at a detailed level by matching location data from smartphones to automatic transit vehicle location (AVL) data and by identifying all out-of-vehicle and in-vehicle portions of the passengers trips. High-resolution travel times and their relationships with the timetable are then derived. Approaches are presented for processing relatively sparse smartphone location data in dense transit networks with many overlapping bus routes, distinguishing waits and transfers from non-travel related activities, and tracking underground travel in a Metro network. The derived information enables a range of analyses and applications, including the development of user-centric performance measures. Results are presented from an implementation and deployment of the system on San Francisco’s Muni network. Based on 103 ground-truth passenger trips, the detection accuracy is found to be approximately 93%. A set of example applications and findings presented in this paper underscore the value of the previously unattainable high-resolution traveler-vehicle coupled movements on a large-scale basis.