Generalising Travel Time Prediction To Varying Route Choices In Urban Networks
For urban traffic management, this model enables accurate travel time prediction under non-recurring route choices, overcoming the limitation of existing methods that assume fixed demand patterns.
GenTTP predicts travel times and flows under varying route choices, achieving accurate predictions where prior graph neural network methods fail. It generalizes across different route assignments, addressing a critical gap in urban traffic prediction.
Previous methods that predict system-wide travel time, predominantly grounded in graph neural networks, remain limited to typical and recurring demand patterns. While they successfully predict future congestion following daily commute, they inherently approximate a single demand realisation and fail to capture varying route choices. In this work, we propose a Generalised Travel Time Predictor (GenTTP) that successfully differentiates route choices and offers accurate flow and travel time predictions. Our framework learns to uncover complex spatiotemporal traffic patterns and microscopic relationships between route choices and the resulting travel times. This addresses a critical gap: the lack of travel time prediction models that generalise across varying route assignments, where the same demand can produce substantially different network-wide outcomes depending on how travellers are distributed over available paths.