mlsanityData to 14 Oct 2026

Generalising Travel Time Prediction To Varying Route Choices In Urban Networks

arXiv 2605.06918 ↗First posted 7 May 2026cs.MA cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 24 references that are in the map. First posted on arXiv 7 May 2026.

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12 of 24 references.

References · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  4. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  5. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 434
  6. 2021Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecastingcited by 67
  7. 2021Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecastingcited by 23
  8. 2023STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecastingcited by 12
  9. 2020InTAS -- The Ingolstadt Traffic Scenario for SUMOcited by 11
  10. 2019Incrementally Improving Graph WaveNet Performance on Traffic Predictioncited by 10
  11. 2025URB -- Urban Routing Benchmark for RL-equipped Connected Autonomous Vehiclescited by 4
  12. 2025Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignmentcited by 3

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