Mobile ticketing apps have become increasingly popular among transit agencies due to their cost efficiency and ability to streamline payments. Beyond operational efficiencies, these apps also generate vast travel data with the potential to support transit agencies in decision-making. However, this data contains incomplete trip information and suffers from representation bias. Several questions remain unanswered: Is this data a statistically representative sample of all transit riders? What are its potential applications? This research will address this gap by evaluating the potential applications and representativeness of app data. The project focuses on ZipPass, a mobile ticketing app used by Unitrans in Davis, California. Within six months of launch, ZipPass has already generated over 350,000 spatial activation records. Researchers devised a strategy to integrate ZipPass data with the onboard transit survey and the UC Davis campus travel survey. They also plan to conduct a targeted survey of active ZipPass users to supplement rider-specific and trip-level information. The team will explore how ZipPass data, along with support from supplementary data sources, can be used for two potential applications to support the agency: (1) estimating transit ridership and (2) understanding riders' origin-destinations. This research will study the reliability and usability of mobile ticketing app data for transit analytics by assessing its quality after augmenting the data with other existing resources to increase contextual information. The research will provide valuable insights to transit agencies looking to harness mobile ticketing data for operational purposes. Since periodic onboard transit surveys are required for federal funding, both mobile ticketing data and transit survey data are available to agencies at no extra expense. Small agencies can leverage our findings to integrate at least these two datasets and effectively utilize them for operational improvement. The project will create a framework for them to integrate mobile ticketing data with periodic transit surveys to support their transit planning and decision-making. While Unitrans serves as our primary case study, the research is designed to be applicable and scalable to transit agencies nationwide.