Most of the current intermodal traveller information applications still consider one-way journeys only. For the calculation of a roundtrip two simple one-way trips are taken into account. However, most of the journeys do not consist of simple one-way trips, as a traveller usually returns to the starting point (home, office for business trips, etc.). Also, arc cost – travel time in general – tend to be time dependent, which means that the cost of the optimum roundtrip is not necessarily the double of the cost of the optimum one-way trip. The diverse arc cost values can be based on dynamic and/or historic data. A lot of effort is spent in order to obtain and store this type of data, which is necessary for describing real-time traffic states and to make forecasts. So the next step is to make better use of this data by integrating them into the calculation of trips that take place in the future. A multimodal information service usually works as follows: The traveller indicates his starting point, his destination and the desired time of departure or arrival. The system then computes the optimum trips for private modes (car, bicycle, etc.) and public transport (all modes). It is up to the user to compare the different possibilities and to decide which one to take. Dynamic data are integrated where available. The possibility of switching between different transportation modes during the trip is rarely offered. The main reasons are the difference in the models (different graphic levels are used for different modes) and the different parameters for describing the arc cost for the different modes. Roundtrips can usually be computed for certain modes, especially for public transport, which relies on static timetable data. In this case, the intermodality is limited to some or several public transport modes of a certain region. More and more service providers offer the possibility to optimise a door-to-door trip, taking into account the time necessary to reach the first transport mode used, as well as the time to reach the destination from the last stop. This paper describes the importance of computing entire intermodal roundtrips rather than two one-way trips, as arc cost tend to vary depending on different parameters (time of the day, special events, etc.). Having more and more dynamic and historic data at hand, it should be used to the maximum possible, in order to optimise mobility habits. Computing intermodal roundtrips also means taking into account several constraints that may arise on the way, like the dependence on a certain mode or leaving behind a private vehicle at certain nodes of the network. The main constraints are pointed out, along with their importance for the calculation.