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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.

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This paper addresses a critical gap in applying semantic enrichment for online news text classification using large language models (LLMs) in fast-paced newsroom environments. While LLMs excel in static text classification tasks, they struggle in real-time scenarios where news topics and narratives evolve rapidly. The dynamic nature of news, with frequent introductions of new concepts and events, challenges pre-trained models, which often fail to adapt quickly to changes. Additionally, the potential of ontology-based semantic enrichment to enhance model adaptability in these contexts has been underexplored. To address these challenges, we propose a novel supervised news classification system that incorporates semantic enrichment to enhance real-time adaptability. This approach bridges the gap between static language models and the dynamic nature of modern newsrooms. The system operates on an adaptive prequential learning framework, continuously assessing model performance on incoming data streams to simulate real-time newsroom decision-making. It supports diverse content formats - text, images, audio, and video - and multiple languages, aligning with the demands of digital journalism. We explore three strategies for deploying LLMs in this dynamic environment: using pre-trained models directly, fine-tuning classifier layers while freezing the initial layers to accommodate new data, and continuously fine-tuning the entire model using real-time feedback combined with data selected based on specified criteria to enhance adaptability and learning over time. These approaches are evaluated incrementally as new data is introduced, reflecting real-time news cycles. Our findings demonstrate that ontology-based semantic enrichment consistently improves classification performance, enabling models to adapt effectively to emerging topics and evolving contexts. This study highlights the critical role of semantic enrichment, prequential evaluation, and continuous learning in building robust and adaptive news classification systems capable of thriving in the rapidly evolving digital news landscape. By augmenting news content with third-party ontology-based knowledge, our system provides deeper contextual understanding, enabling LLMs to navigate emerging topics and shifting narratives more effectively. Copyright © 2024 for this paper by its authors.

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