Salvador Urban Network Transportation (SUNT): A Landmark Spatiotemporal Dataset for Public Transportation

In the referenced manuscript, we introduced SUNT, to our knowledge the richest open urban mobility dataset, integrating continuously collected passenger and vehicle data across three public transport systems in Salvador, Brazil, namely regular buses, subway, and BRT. SUNT covers daily activity for about 700,000 passengers and approximately 2,000 vehicles operating over nearly 400 lines and close to 3,000 stops and stations, with collection from March 2024 to March 2025 at subminute resolution. The article details data acquisition and organization, including technical validation through descriptive statistics and quality checks, as well as the implementation of a trip chaining to build Origin–Destination flows. We present a large scale, real world testbed for reasoning over graphs, sequences, and uncertainty in continuous time, supporting research in scalable online learning, distribution shift adaptation, and decision making under constraints across logistics, energy, and other infrastructure domains. At the conference we will show results with new methods and recent state of the art in Graph Neural Networks, Transformer based and Recurrent models, Reinforcement Learning, Multiagent methods, Continual Learning, and Foundation models. Researchers across AI can use SUNT and the accompanying models as a shared benchmark, enabling comparable, reproducible, and transferable evaluation and accelerating progress in temporal modeling, relational reasoning, and decision making under uncertainty.