AI RESEARCH
Generalising Travel Time Prediction To Varying Route Choices In Urban Networks
arXiv CS.LG
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ArXi:2605.06918v1 Announce Type: cross 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.