mlsanityData to 7 Oct 2026

Graph Neural Networks for Traffic Forecasting

arXiv 2104.13096 ↗First posted 27 Apr 2021cs.LG cs.AIcited by 7

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Abstract (arXiv)

The significant increase in world population and urbanisation has brought several important challenges, in particular regarding the sustainability, maintenance and planning of urban mobility. At the same time, the exponential increase of computing capability and of available sensor and location data have offered the potential for innovative solutions to these challenges. In this work, we focus on the challenge of traffic forecasting and review the recent development and application of graph neural networks (GNN) to this problem. GNNs are a class of deep learning methods that directly process the input as graph data. This leverages more directly the spatial dependencies of traffic data and makes use of the advantages of deep learning producing state-of-the-art results. We introduce and review the emerging topic of GNNs, including their most common variants, with a focus on its application to traffic forecasting. We address the different ways of modelling traffic forecasting as a (temporal) graph, the different approaches developed so far to combine the graph and temporal learning components, as well as current limitations and research opportunities.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 49 of its 99 references that are in the map. First posted on arXiv 27 Apr 2021.

Builds on

49 of 99 references.

References · 49

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37 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 4; 2026: 1.

7 papers.

  1. 2023Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approachcited by 0
  2. 2024Graph Based Traffic Analysis and Delay Predictioncited by 0
  3. 2025Spatially Directional Dual-Attention GAT for Spatial Fluoride Health Risk Modelingcited by 0
  4. 2025Beyond Attention: Learning Spatio-Temporal Dynamics with Emergent Interpretable Topologiescited by 0
  5. 2025Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoningcited by 0
  6. 2025Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semanticscited by 0
  7. 2026From Time Series to State: Situation-Aware Modeling for Air Traffic Flow Predictioncited by 0