DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting

Binqing Wu; Ling Chen

DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2307.00518 v1 pith:FGTUMTX7 submitted 2023-07-02 cs.LG cs.AI

Binqing Wu , Ling Chen This is my paper · ORCID

classification cs.LGcs.AI

keywords dependenciesdynamicspatialtemporalcrossgraphtrafficconstruction

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original abstract

Traffic forecasting is essential to intelligent transportation systems, which is challenging due to the complicated spatial and temporal dependencies within a road network. Existing works usually learn spatial and temporal dependencies separately, ignoring the dependencies crossing spatial and temporal dimensions. In this paper, we propose DSTCGCN, a dynamic spatial-temporal cross graph convolution network to learn dynamic spatial and temporal dependencies jointly via graphs for traffic forecasting. Specifically, we introduce a fast Fourier transform (FFT) based attentive selector to choose relevant time steps for each time step based on time-varying traffic data. Given the selected time steps, we introduce a dynamic cross graph construction module, consisting of the spatial graph construction, temporal connection graph construction, and fusion modules, to learn dynamic spatial-temporal cross dependencies without pre-defined priors. Extensive experiments on six real-world datasets demonstrate that DSTCGCN achieves the state-of-the-art performance.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting
cs.LG 2025-09 conditional novelty 6.0 of 10

MillGNN learns delayed (lead-lag) influences between time series and between groups of series at multiple grouping scales, reporting state-of-the-art forecast errors on 11 benchmarks.
ST-Hyper: Learning High-Order Dependencies Across Multiple Spatial-Temporal Scales for Multivariate Time Series Forecasting
cs.LG 2025-09 conditional novelty 5.0 of 10

ST-Hyper combines spatial-temporal pyramid feature extraction with adaptive sparse hypergraph learning and tri-phase propagation to achieve state-of-the-art results on six multivariate time series forecasting benchmarks.

Pith tools