Resumen
The smart cards conquered a serious space in the traffic in the past years. The cards are used like an automatic fare collection system by the different public transport companies. The fare collection is easier, faster and last but not least more efficient with this method. However the generated data are utilizable for other aims apart from the original aim. The origin-destination matrix plays a key role in a transport company's life. We may adapt to the travel demands efficiently in the knowledge of the matrix. Thanks to the proper service the passengers will be satisfied, and they use the service with pleasure. The additional benefit, that the unnecessary capacities can be forced back in the knowledge of the demand, and thereby the expenses of the service provider's decrease. The origin-destination matrix was able to create only with classical counting and interview methods till now. Their disadvantage, that the production consumes considerable workforce and time with a method like this, and hereby the costs are higher. The counting concern to a given time only and the sampling proportion compared to the number of travels of the year is quite slight. Because of that the distortion effect is considerable, the matrix cannot reflect the effects of an intervention. These effects get into the system only after the next counting. The penetration of the smart cards increases, the card user passengers? number increases continuously. Thanks to that, not only the cost efficiency of the fare collection is growing, but at the same time the statistical sample size too. If more passengers from young persons to older ones accept the usage of the cards, we may receive more accurate picture about the transportation network. If the fare collection system was built up somewhere, the origin-destination matrix can be manufactured with lower expenses, and because of the continuous usage the matrix will be current always, the changes become traceable. We may know the demands in all the moments, the origin-destination matrix may change dynamically in function of the weather, time of the day, season and so on. The check-out data can be manufactured from exclusively check in data with different algorithms. The passenger's boarding and alighting places become known on the given route. Linking the sections belonging to a single trip and defining the target point, the origin destination matrix can be developed.