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An Overview of the Effectiveness of Graph Learning Methods for Traffic Demand Forecasting

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Luong-Chi Trung
Chung-Thai Kiet
Nguyen-Huu An
Dung-Cam Quang
Published in
Multi-disciplinary Trends in Artificial Intelligence
Publisher
Springer Nature Singapore

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Abstract

This study delves into the effectiveness of graph learning methods for traffic demand forecasting, a critical component of Intelligent Transportation Systems (ITS). By benchmarking six prominent models—CCRNN, MSDR, MVFN, DMGL, DDGCRN, and PDG2Seq—using both public and newly collected datasets, the research highlights the impact of different graph construction strategies. The study categorizes these models into static, adaptive, and dynamic graph approaches, demonstrating that dynamic graph learning excels in capturing complex spatial and temporal relationships. The findings reveal that dynamic models consistently outperform static and adaptive baselines, with improvements of 7.22% in MAE, 3.95% in RMSE, and 0.48% in PCC. The research also addresses key challenges and future directions, such as model efficiency, dataset diversity, and the need for globally representative datasets. This comprehensive analysis provides valuable insights for professionals seeking to enhance traffic demand forecasting accuracy and robustness.
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previous chapter Spread-Learned Spatial Features to Improve Tick-Shape Networks
next chapter Decomposition-Based Optimization of Multi-camera Networks for Coverage Maximization Problem
Title
An Overview of the Effectiveness of Graph Learning Methods for Traffic Demand Forecasting
Authors
Luong-Chi Trung
Chung-Thai Kiet
Nguyen-Huu An
Dung-Cam Quang
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Multi-disciplinary Trends in Artificial Intelligence

Print ISBN: 978-981-9549-56-6

Electronic ISBN: 978-981-9549-57-3

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-4957-3_11
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