Efficient and well organized public transport is a requirement for smooth traffic and sensible urban structure. Traffic planning requires extensive data of travel behavior. The use of public transport can be examined with data collected from smart cards. Smart card data has many benefits over traditional data collection methods. Data is generated as a byproduct of payment transactions, so there are no extra costs in collection and it contains nearly every trip. This thesis documents the implementation of a method for refining Helsinki Regional Transport (HSL) smart card data. The method estimates alighting stops of passengers. Boarding and alighting information can be used to form origin-destination matrices and line profiles and to calculate key statistics, such as average trip lengths. In the HSL area, passengers are not required to show their smart cards every time they board a metro, train or tram. Therefore only bus transport can be reliably estimated. Alighting estimation method used in this thesis is based on research published in international journals. Card ID, boarding time and information about bus line and stop are collected with every smart card transaction. Card ID is used to link consecutive boardings that are paid with the same card. Owner can not be determined from card ID. Alighting stop is estimated from the next boarding recorded with the same card. Alightings that are not be determined this way and boardings not included in the smart card data are accounted for by expanding the estimated trips. Results from the method in this thesis seem reliable and comprehensive. Smart cards are used for 97 % of the trips, alighting stops are found for over 70 % of the boardings and number of passengers derived with the method match well with observed data. The method provides information that has not been previously available and it can replace current data collection methods. Results can be made available for transport analysts and planners trough traffic planning tools.