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Heterogeneous Graph Neural Networks for End-to-End Traffic Assignment and Traffic Flow Learning
April 30, 2024, 4:44 a.m. | Tong Liu, Hadi Meidani
cs.LG updates on arXiv.org arxiv.org
Abstract: The traffic assignment problem is one of the significant components of traffic flow analysis for which various solution approaches have been proposed. However, deploying these approaches for large-scale networks poses significant challenges. In this paper, we leverage the power of heterogeneous graph neural networks to propose a novel data-driven approach for end-to-end traffic assignment and traffic flow learning. Our model integrates an adaptive graph attention mechanism with auxiliary "virtual" links connecting origin-destination node pairs, This …
abstract analysis arxiv challenges components cs.lg flow graph graph neural networks however networks neural networks paper power scale solution traffic type
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