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As opposed to public transit agencies' well-developed data generation capabilities, their utilization of their data is often overlooked. This study will tap into the potential of using the GTFS data format from an agency stakeholder perspective to assess transit performance. This format holds data for scheduled transit services, including real-time updates and network organization. The broad adaptation of GTFS by transit agencies (1240 transit networks in 672 locations worldwide) has made it a de-facto standard, making products built on top of it inherently scalable and could potentially be deployed in networks all over the world.

The purpose of this thesis is two-fold; firstly, to explore how specific vulnerability features of nodes in a public transit network can be assessed using graph mining algorithms. Secondly, to develop a pipeline for aggregating GTFS data and fit it into a flow network model. The results include a data-driven framework for vulnerability characterization, a method for fitting GTFS data in a flow network model, and lastly, a definition for reduced flow capacity in a public transit context. Additionally, the results are presented in the setting of Uppsala's network (UL) and visualized with a web-based tool.