The key to the planning of public transport systems is the accurate prediction of the traffic load, or the correct execution of the planning stage assignment. This requires not only a well-functioning assignment method, but also reliable passenger data. Reliable passenger data means a time-dependent origin-destination matrix. To solve the problem of the lack of time-dependent passenger data, we have developed a forecasting method. It consists of three stages. In the first stage, we collect full scope cross-section data. This can be done either with personnel or with an automatic counting system. If personnel are used it costs a lot, and there is the chance for many possible errors. However, the results in most cases are good enough. Automatic counting system can be either a counter machine or even a simple Check in E-ticketing system. In the second stage, we link boarding and alighting. As result, we get the origin-destination matrix for each run. This method is based on the likelihood of alighting at a given stop. In the third stage, we combine origin-destination matrices of the runs through transfers. At this stage we assume that the probability of a transfer between two runs in a given stop is proportional to the travel possibilities in this relation. To view the entire method in practice we proved it in a Hungarian city (Dunaújváros). The results were reliable, so they could be used in the planning process.