Assessing the Impact of Spatial Data Quality on Deep Learning Models for Travel Time Estimation - de A. Correia, Diêgo; L. Silva, Rafael Luciano; Santiago, Vinicius; S. Alves, Jose Matheus; T. Melo, Ruan; R. S. Fernandes, J. Rui; J. Coutinho, Fábio (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd.2026.249667, 2026)
Data quality is fundamental to the reliability of predictive models. In the context of spatial data, understanding how quality dimensions influence robustness is critical for intelligent transportation systems. This study investigates the impact of spatial data quality on Deep Learning models for Travel Time Estimation (TTE). The methodology encompasses the steps of data pollution, model training, and testing under two complementary scenarios: polluted training and test data. We evaluate three models using the Porto Taxi dataset while considering the impact of the Positional Accuracy and Completeness quality dimensions defined in ISO 19157-1:2023. The results indicate that the impact of data quality degradation varies according to both model architecture and application phase.