Travel Time Prediction with Machine Learning for Public Bus Transportation in Florianópolis

• Leonardo Villamarin de Souza UFF
• Thiago Rodrigues da Motta Fagundes UFF
• Tielle da Silva Alexandre UFF
• Flavia Bernardini UFF

DOI: https://doi.org/10.5753/lasdigov.2025.8894

Abstract

In the Smart Cities context, planning public transport services is essential in cities with high population density. To this end, having access to Travel Time Prediction (TTP) for bus lines is important for identifying bottlenecks in the city. The TTP has been explored in the literature over the last 2 decades, but there is a significant gap in tools to support scientists to process data from this domain and to build predictive models with Machine Learning (ML). This work presents a simplified process for constructing a travel time predictor, which we model, exploring possible strategies that small city governments can adopt. The process is evaluated using real data from the city of Florianópolis.

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