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 two groups: school students, and passengers traversing Stockholm's inner city. 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. 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.

While social segregation is often assessed using static data concerning residential areas, the extent to which people with diverse background travel to the same destinations may offer an additional perspective on the extent of urban segregation. This study further contributes to the measurement of activity-based social segregation between multiple groups using public transport smart card data. In particular, social segregation is quantified using the ordinal information theory index to measure the income group mix at public transport journey destination zones. The method is applied to the public transport smart card data of Stockholm County, Sweden. Applying the index on 2017–2020 data sets for a selected week, shows significant differences between income groups’ segregation along the radial public transport corridors following the opening of a major rail project in the summer of 2017. The overall slight decrease in segregation over the years can be linked to declining segregation in the city center as a travel destination and its public transport hubs. Increasing zonal segregation is observed in suburban and rural zones with commuter train stations. This method helps to quantify social segregation, enriching the analysis of urban segregation and can aid in evaluating policies based on the dynamics of social life.

This paper develops a method for analysing the elasticity of travel demand to public transport fares. The methodology utilizes public transport smartcard data for collecting disaggregate full population data about passengers’ travel behaviour. The study extends previous work by deriving specific fare elasticities for distinct socioeconomic (e.g., car ownership and income) groups and public transport modes (metro, trains and buses), and by considering the directionality of the fare change. The case study involves a public transport fare policy introduced by the regional administration of Stockholm County in January 2017, where the zonal fare system for single-trip tickets was replaced by a flat-fare policy. The overall fare elasticity of travel funds is found to be −0.46. User sensitivity grows along with the journey distance. Metro users demonstrate the lowest sensitivity, followed by bus and commuter train riders. Low socioeconomic groups, in particular with respect to car ownership, tend to be less sensitive than the high-factor groups. In addition to the direct effect of changed fares, simplification and unification of the fare scheme appears to have substantially contributed to its attractiveness. The flat fare may allow the geographic disparity of public transport travel to be reduced and new users to be attracted from remote areas who are more prone to own cars.