<p>Predicting lane‑based hourly traffic flow in urban networks with limited data coverage remains a critical challenge for network and traffic management. This paper develops a data‑driven methodology that leverages publicly available travel time feeds from Google Maps API, spatially matched to detector locations, and contextual features including the number of lanes, the capacity per lane, the signalization status, the functional class, the period of day (peak/off‑peak), and day of week, as well as the emergence of a disturbance and/or extreme weather conditions to predict lane-based hourly traffic flow at locations within the urban transport network of the Athens wider metropolitan area. A variety of supervised learning techniques are trained, and models’ interpretability is revealed by making use of an approach combining the SHAP summary plot and the permutation importance vs Mean Partial Dependence plot. The results show that Gradient Boosting Decision Tree yields the best performance, demonstrating that even aggregated and crowd-sourced travel times inputs can provide a reasonable approximation of lane-based hourly traffic flow without dense sensor infrastructures. Feature importance insights identified signalization and longer travel times as features that lead to increased predictions, and additional lanes, counterintuitively, as a feature that leads to lower hourly traffic flow. Out‑of‑sample validation on ten previously unseen locations from the pNEUMA dataset demonstrates the model’s robustness to unseen traffic patterns without retraining. Future work will investigate transfer learning across different cities, as well as the integration of real‑time incident and weather feeds in the model for improved accuracy.</p>