Researchers and planners have taken great interest in the rich data-resource that smartphone and GPS travel surveys can now produce. The interpretation of this data has become a popular topic with methods such as transit itinerary inference (TII) from travel survey data and GTFS emerging as useful tools in the field of travel behavior analysis. This exploratory research develops metrics to quantify a characteristic of GTFS data that complicates the overlay processing of travel survey GPS points and bus route geometries in TII: the spatiotemporal overlap of bus routes in the GTFS record. Accurate route inference is difficult in regions where rider data coincides with overlapping routes and various TII approaches have been tested to address this challenge. In this research, detecting overlap, and quantifying the degree of overlap on road links is achieved in 5 study regions through the application of two proposed measures: The Overlapping Routes on Links (OROL) index, and the Probability of Passage (POP) score. The latter’s output is seen as one way to improve route matching rates in TII. These measures build off the traditional Line Overlapping Index (LOI) and improve upon it by providing previously unobtainable road-link level detail; the OROL index, in fact, represents a spatially precise decomposition of the LOI. To ensure accurate analysis between networks, an additional novel procedure is developed that converts GTFS data into a simplified stand-in road network representation, thus providing a base layer for disaggregate network measures, and replacing the need for additional road network sources entirely.