Through the use of Intermodal Transport Control Systems (lTCS) and passenger information systems, there are more and more digital systems in public transport, which have a high information potential. For scheduling tasks as well as for information services, it is not only the knowledge and transmission of the current traffic situation that is relevant, but also a reliable forecast of arrivals. This allows for improved real-time travel planning and assistance, including travel arrangements, and passenger information. Despite all the qualities achieved, the forecast quality is still unsatisfactory, especially in networked systems with a dense sequence of driving sequences and strong IV influences. This applies both to the forecasts for the time of arrival and the reliable prediction of the order in which vehicles arrive at the stop, which is important for passengers with limited mobility.

Until now, actual data for a forecast period has been extrapolated unchanged to the subsequent stop points without having to carry out forecasts for a delay recovery or increase in delay
a reliable time of arrival (order at stops) and - disposition measures for connection. With increasing forecasting horizons, the probability of occurrence and reliability have thus so far decreased significantly. Two main influencing factors are first that current timetable situations are often only updated without taking into account the reserves not included in the timetable; this tends to lead to an excessively long journey time.
Moreover, external influences caused by road traffic or unavailable timetable routes in the rail network tend to lead to an underestimated travel time estimate.