GeoPython 2022

The literature on mobility has delved into the close link between mobility patterns and social inequality. Using data from the public transport electronic payment system, census data, and the location of slums, this paper carries out an empirical analysis of travel patterns of different socioeconomic groups that use public transport in the Metropolitan Region of Buenos Aires. To perform this analysis, we process data from the public transport electronic payment system in order to infer destinations and create daily trip chains for each user. Since all transactions are georeferenced at the origin of each trip, we assign users a socioeconomic level constructed with census data considering the location of the first trip of the day (which presumably corresponds to a stop close to their homes). Then, we calculate trip distances, develop origin-destination matrices, and create travel pattern maps for the different socioeconomic groups. We found out that trips from low socioeconomic groups are more dispersed in the territory, while trips from high socioeconomic groups are more concentrated in the central administrative and business area of the city. Lower-income groups tend to have destinations that are less connected, which results in longer trips and more transfers, without using a more efficient multimodal strategy. The modal split is also characterized by greater use of the bus (instead of using metro or train) and, even when transfers occur, there is a greater probability of combining two buses instead of metro and rail. In the case of users who live near vulnerable neighborhoods, we observe that the trips are shorter, more direct, and with fewer transfers than those of users of low socioeconomic status.