ML Anthology

Reinventing Node-Centric Traffic Forecasting for Improved Accuracy and Efficiency

Xu Liu, Yuxuan Liang, Chao Huang, Hengchang Hu, Yushi Cao, Bryan Hooi, Roger Zimmermann
ECML-PKDD 2024 pp. 21-38
doi:10.1007/978-3-031-70352-2_2 /ecmlpkdd/2024/liu2024ecmlpkdd-reinventing/

Abstract

Traffic forecasting is a crucial application in smart city efforts. After revisiting the existing literature on deep learning-based traffic forecasting methods, we identify two primary research approaches: node-centric and graph-centric. Node-centric methods focus on constructing spatial features through preprocessing and modeling spatial correlations in the input space. In contrast, graph-centric methods mainly rely on graph neural networks to capture spatial correlations in the latent space. We perform empirical evaluations to identify the pros and cons of each: node methods excel in efficiency while graph methods demonstrate better performance. Based on this, we propose a simple yet effective node-centric framework, named SimST, which overcomes the drawbacks of node-centric methods and enhances their efficiency. Extensive experiments show that SimST achieves performance on par with graph-centric methods while exhibiting up to 39 times inference speedup.

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Cite

Text

Liu et al. "Reinventing Node-Centric Traffic Forecasting for Improved Accuracy and Efficiency." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024. doi:10.1007/978-3-031-70352-2_2

Markdown

[Liu et al. "Reinventing Node-Centric Traffic Forecasting for Improved Accuracy and Efficiency." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024.](https://mlanthology.org/ecmlpkdd/2024/liu2024ecmlpkdd-reinventing/) doi:10.1007/978-3-031-70352-2_2

BibTeX

@inproceedings{liu2024ecmlpkdd-reinventing,
  title     = {{Reinventing Node-Centric Traffic Forecasting for Improved Accuracy and Efficiency}},
  author    = {Liu, Xu and Liang, Yuxuan and Huang, Chao and Hu, Hengchang and Cao, Yushi and Hooi, Bryan and Zimmermann, Roger},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2024},
  pages     = {21-38},
  doi       = {10.1007/978-3-031-70352-2_2},
  url       = {https://mlanthology.org/ecmlpkdd/2024/liu2024ecmlpkdd-reinventing/}
}