Efficient public transportation management is essential for the development of large urban centers, providing several benefits such as comprehensive coverage of population mobility, reduction of transport costs, better control of traffic congestion, and significant reduction of environmental impact limiting gas emissions and pollution. Realizing these benefits requires a deeply understanding the population and transit patterns and the adoption of approaches to model multiple relations and characteristics efficiently. This work addresses these challenges by providing a novel dataset that includes various public transportation components from three different systems: regular buses, subway, and BRT (Bus Rapid Transit). Our dataset comprises daily information from about 700,000 passengers in Salvador, one of Brazil’s largest cities, and local public transportation data with approximately 2,000 vehicles operating across nearly 400 lines, connecting almost 3,000 stops and stations. With data collected from March 2024 to March 2025 at a frequency lower than one minute, SUNT stands as one of the largest, most comprehensive, and openly available urban datasets in the literature.

Hemophilia is a genetic disorder characterized by mutations in coagulation factor proteins, resulting in impaired blood coagulation. Despite recent advances in diagnosing and treating Hemophilia, the precise function of each residue within such proteins remains unclear. In this work, aiming to provide a crucial step to addressing this complex problem, we present a systematic approach that organizes the coagulation factor proteins into graphs to be later modeled by novel Graph Neural Networks (GNN). In our graphs, nodes represent amino acids, which can be connected by proximity in the three-dimensional structure. Our experiments on FVIII (coagulation factor VIII) proteins, responsible for causing Hemophilia A, presented significant results in predicting severe and mild forms of the disease. Moreover, focused on supporting the advance of novel recombinant therapeutic FVIII proteins, we adapted our study to predict the activity and expression of more than 300 in vitro alanine mutations. Besides innovating the form in which hemophilia is organized and modeled, this work presents a GNN architecture specifically designed to optimize the classification of FVIII proteins. Our results emphasize how graph-based approaches support a deeper understanding of Hemophilia A, thus providing a solid scientific basis for further research in this field.