Generalising Travel Time Prediction To Varying Route Choices In Urban Networks
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 ...
| Authors | Łukasz Gorczyca, Kacper Drozd, Michał Bujak et al. |
|---|---|
| Primary category | cs.MA |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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