This article presents a programmable method for the repeated automatic determination of routes of public transport trips by bus, tram or train, for a daily updated simulation of these trips in a microscopic traffic simulation model, e.g. SUMO. As the simulation model has to provide real-time data for a simultaneous visualization of vehicles in an urban digital twin, high-resolution data regarding location, heading and velocity is required. Therefore, the accurate route of every trip and the corresponding stops and timetable are required as simulation input. Currently, these data cannot be reliably obtained from a single public data source such as OpenStreetMap (OSM) and General Transit Feed Specification (GTFS). The method enables the calculation of actual and accurate routes of individual trips on the basis of the OSM networks and GTFS schedules, by clustering and matching stops, and optimizing the routes with shortest path algorithms used in SUMO and Python. The method is demonstrated by numerous quantitative examples obtained from an existing SUMO model which is currently built up and maps the city of Magdeburg.