Customer-oriented optimization of public transport needs data about the passengers in order to obtain realistic models. Current models take passengers’ data into account by using the following two-phase approach: In a first phase, routes for the passengers are determined. In a second phase, the actual planning of lines, timetables, etc., takes place using the knowledge on which routes passengers want to travel from the results of the first phase. However, the actual route a passenger will take strongly depends on the timetable, which is not yet known in the first phase. Hence, the two-phase approach finds non-optimal solutions in many cases. In this paper we study the integrated problem of determining a timetable and the passengers’ routes simultaneously. We investigate the computational complexity of the problem and present solution approaches which are tested on close-to-real-world data.