BigST: Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks

Summary: BigST is a linear-complexity STGNN that encodes node-wise long-range sequences via a precomputable low-dimensional feature extractor and a linearized global spatial convolution to distill time-varying graph structure. Scales to ~100k-node road networks, delivering improved accuracy and runtime for long-horizon traffic forecasting versus quadratic-cost baselines. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13358
Venue: VLDB
Year: 2024
Pagerank: 7.3355287e-05
Overall Rank: 3,234 | 77.51%
DOI: 10.14778/3641204.3641217

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,593	Scalable Pre-Training of Compact Urban Spatio-Temporal Predictive Models on Large-Scale Multi-Domain Data	2025	VLDB	4.1945683e-05
10,604	GraphSparseNet: a Novel Method for Large Scale Traffic Flow Prediction	2025	VLDB	4.1945683e-05
10,687	Towards Pattern-aware Data Augmentation for Temporal Knowledge Graph Completion	2025	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
2,290	TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data	2022	VLDB	9.0934125e-05
3,411	MDTP: A Multi-source Deep Traffic Prediction Framework over Spatio-Temporal Trajectory Data	2021	VLDB	7.1253055e-05
4,206	Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting	2022	VLDB	6.3595566e-05
5,026	AutoCTS: Automated Correlated Time Series Forecasting	2022	VLDB	5.7528419e-05
5,895	METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting	2022	VLDB	5.2857247e-05

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