Integrating Fixed Sensors and Floating Car Data for Traffic Flow Estimation in Bologna

Efficient urban monitoring requires methods able to estimate traffic flows on road segments with limited or no sensor coverage. This study validates a traffic flow-scaling model that combines fixed sensors and Floating Car Data (FCD) in Bologna across 11 sections. Tree-based Machine Learning (ML) models, particularly XGBoost, demonstrated excellent performance after hyperparameter optimization. Using the scaled data, a short-term forecasting task (+1h and +4h) showed that tree-based models outperform a neural network based Long Short-Term Memory (LSTM) model in terms of accuracy and robustness. Results indicate that the integration of floating car data and fixed sensors enables accurate spatio-temporal traffic estimation, providing a scalable and cost-effective strategy for urban mobility planning and proactive traffic management.