On-board crowding in public transportation has a significant impact on passengers' travel experience. However, there is little knowledge of how different passenger groups contribute to on-board crowding. Empirical knowledge of specific passenger groups' impact on the system facilitates more effective tuning of policy instruments such as new fare structures, dedicated public transportation services, infrastructure investments, and capacity provision. We propose a method to capture the crowding contributions from selected passenger groups by means of smart card data analytics. Two crowding contribution metrics at the passenger journey level are proposed: (1) time-weighted contribution to load factor and (2) maximum contribution to load factor. We apply the proposed method to the multimodal public transportation system of Region Stockholm, Sweden. We demonstrate the method for three groups: school students, passengers traversing Stockholm's inner city, and airport passengers. Our findings indicate that school students and passengers traversing the inner city have similar crowding contributions, utilizing 15% and 11% of the seating capacity across all modes during the AM and the PM peak, respectively. Airport passengers have significantly lower crowding contributions, utilizing less than 1% of the seating capacity during both peak periods. The commuter rail network, as well as some of the areas neighboring it, experience on average more than 70% and 90% utilization of their seating capacity during the AM peak, by school students and passengers traversing the inner city, respectively.
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