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cs.LG updates on arXiv.org

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Learning from user's behaviour of some well-known congest...
Isolda Cardo · 2026-05-08 · via cs.LG updates on arXiv.org

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Abstract:The traffic assignment problem (TAP) aims to predict how traffic flows distribute themselves across a road network, traditionally requiring computationally expensive iterative simulations to reach a user equilibrium (UE) where no driver can unilaterally reduce their travel time. Recent developments in machine learning (ML), particularly Graph Neural Networks (GNNs) and hybrid approaches, aim to solve this faster while maintaining accuracy
Comments: 30 pages, 8 figures, 7 tables
Subjects: Optimization and Control (math.OC); Machine Learning (cs.LG)
MSC classes: 90B20, 68T20, 90C33
Cite as: arXiv:2508.14804 [math.OC]
  (or arXiv:2508.14804v2 [math.OC] for this version)
  https://doi.org/10.48550/arXiv.2508.14804

arXiv-issued DOI via DataCite

Submission history

From: Isolda Cardoso [view email]
[v1] Wed, 20 Aug 2025 15:53:13 UTC (1,553 KB)
[v2] Wed, 6 May 2026 22:59:18 UTC (2,826 KB)