This thesis tries to establish two indicators for public transit analysis. These indicators are based in a thorough reading of the literature as well as an explorative data analysis phase based on openly available data. These indicators were tested on a case study, for which Heidelberg, Germany was selected due to the familiarity and local knowledge of the author. These indicators are based on a conceptualisation of reach based on grid cells and an approximation of an inverse closeness centrality. A difference in favourable and unfavourable departure times was used to establish an indicator for the need to plan. For the purpose of analysis a `h3` hexagonal grid was laid over the study area, and bi-modal travel times were calculated between each cell pairing with `r5py`. Population data was used to filter cells. A literature gap was identified in the daily variation of travel time analysis, so for each of the two indicators they were computed for every hour of the day, covering a full 24 hours. The travel time indicator displays expected features of a travel time dataset, but lacks more informed choices of itinerary scenarious for a deeper analysis. The temporal variation however suggests that not all areas of a city are necessarily served equally well during the day, and specific features of transit infrastructure can even disadvantage otherwise central cells in off peak travel. The planning indicator, unfortunately, turned out to be hard to operationalise correctly, based on limited routing options. Both indicators need further work in fine tuning and potentially the set of indicators needs to grow to sufficiently describe a public transit system in all its complexity.