Modelling route-level interzonal travel time using GPS trajectories

Activity-based models (ABMs) require accurate travel-time estimates for accessibility calculations, yet many implementations rely on static routing outputs that fail to capture temporal congestion dynamics due to limited high-resolution data. This paper develops route-level travel-speed prediction models using GPS trajectory data from 48 vehicles in Flanders, Belgium. GPS trajectories are integrated with OpenStreetMap and land-use data through destination-based segmentation, in which trips from fixed origins are cumulatively segmented at zone crossings. To capture behavioural differences by trip length, separate Gamma regression models are estimated for short (≤5 km) and long (>5 km) trips using temporal, network, and spatial variables. The models achieve strong predictive performance (pseudo R² = 0.57 for short trips; 0.85 for long trips). Results show distinct congestion patterns: short trips peak around 9 AM with substantial night time speed recovery, while long trips exhibit weaker temporal variation and afternoon peaks. Road hierarchy effects are stronger for long trips, whereas intersection effects dominate short trips. The proposed approach enables the integration of dynamic, time-dependent accessibility measures into ABM frameworks.