This paper focuses on short-term traffic speed projections, with the goal of estimating vehicle speeds within specific time intervals using advanced machine learning models. The research proposes the use of graph-based neural models for the prediction of short-term speeds of public buses on a segment of Avenida Alameda Libertador Bernardo O'Higgins in Santiago, Chile. This paper describes the methodology employed, including data collection, preprocessing, model training, and performance evaluation. The findings and recommendations derived from the models have the potential to offer valuable information to improve traffic efficiency and urban mobility planning. The study highlights the importance of leveraging technological advances and local data to develop more accurate and efficient traffic forecasting models, ultimately benefiting public and private entities involved in transportation management.