In smart city data engineering, accurate traffic forecasting is crucial for effective urban planning and management, as well as for developing Urban Digital Twins (UDTs). Extending prior work [1] on multistep, multi-scale traffic flow forecasting, this paper explores integrating weather data into traffic forecasting models using graph neural networks (GNNs) to assess its impact. The study focuses on selecting features and time ranges for multivariate multistep traffic forecasting. It experiments with GNN setups and compares single-node per Traffic Eye Universal (TEU) configurations with combined nodes. The impact of incorporating weather features on model accuracy and training efficiency is evaluated. In the present setting, with motorized traffic in the city of Osnabrück and weather data available only at the city level, the key findings are that weather data did not improve forecasting accuracy and increased training time. Potential benefits for bicycles and pedestrians are noted as a hypothesis, as data limitations preclude further exploration. The study underscores the efficacy of correlation matrices and mutual information for feature selection, highlighting the importance of capturing non-linear relationships. It suggests that, in this configuration, the exclusive use of TEU data yields the best performance, with the node structure having a negligible impact on results. These findings imply that city-level weather data may not be a predictive value for all traffic types and motivate future research on sensor-level weather and road-surface data, as well as richer feature selection. This study contributes to the understanding of smart city data systems, underscoring the importance of efficient feature selection and the challenges of integrating diverse data sources.
