In this contribution, we present an approach to identify delays within the routes of public transport systems. This approach uses data from an Intermodal Transport Control System (ITCS) and OpenStreetMap to analyze the operation of public transport systems. Within each individual trip, avoidable and unavoidable obstacles like curves and traffic lights are identified, located, and classified as a basis for constructing the best-case scenario of the trip. This best-case scenario is constructed for public transport systems using velocity functions of the vehicle and takes into account unavoidable speed restrictions such as curves. If the best-case scenario offers time savings compared to the original trip, these savings are distributed source-specific among the identified causes via a dynamic weighting system. The computation results in the identification of the length, location and cause of avoidable delays within the transportation system operation. This algorithm can also be used to calculate the time savings from excluding stops in the route planning process. This approach has been successfully used to identify the most time-consuming traffic light systems within the operation of the local tram system in the city of Krefeld, Germany. This research result of increasing the mean velocity is presented in this contribution.