VisiFold: Long-Term Traffic Forecasting via Temporal Folding Graph and Node Visibility

TL;DR

This framework introduces a novel temporal folding graph that consolidates a sequence of temporal snapshots into a single graph and presents a node visibility mechanism that incorporates node-level masking and subgraph sampling to overcome the computational bottleneck imposed by large node counts.

Abstract

Traffic forecasting is a cornerstone of intelligent transportation systems. While existing research has made significant progress in short-term prediction, long-term forecasting remains a largely uncharted and challenging frontier. Extending the prediction horizon intensifies two critical issues: escalating computational resource consumption and increasingly complex spatial-temporal dependencies. Current approaches, which rely on spatial-temporal graphs and process temporal and spatial dimensions separately, suffer from snapshot-stacking inflation and cross-step fragmentation. To overcome these limitations, we propose \textit{VisiFold}. Our framework introduces a novel temporal folding graph that consolidates a sequence of temporal snapshots into a single graph. Furthermore, we present a node visibility mechanism that incorporates node-level masking and subgraph sampling to overcome the computational bottleneck imposed by large node counts. Extensive experiments show that VisiFold not only drastically reduces resource consumption but also outperforms existing baselines in long-term forecasting tasks. Remarkably, even with a high mask ratio of 80\%, VisiFold maintains its performance advantage. By effectively breaking the resource constraints in both temporal and spatial dimensions, our work paves the way for more realistic long-term traffic forecasting. The code is available at~ https://github.com/PlanckChang/VisiFold.

Figures (9)

• Figure 1: From spatial-temporal graph to temporal folding graph. A spatial-temporal graph leads to snapshot-stacking inflation and cross-step fragmentation, thereby constraining the expansion of the forecasting horizon. Temporal folding graph collapses all attributes over time steps into a single node, thereby compressing a sequence of snapshots into a single graph.
• Figure 2: Overview of VisiFold. VisiFold pipeline begins by building a temporal folding graph, from which token embeddings are derived via a linear transformation and fused with other embeddings. This is followed by node-level masking and subgraph sampling. The refined representations are then encoded by a Transformer encoder, and final predictions are generated by an MLP head.
• Figure 3: Resource consumption comparison. The numbers above the bars for Ours are absolute values, while those above the baseline indicate multiples relative to Ours. VisiFold gains a significant superiority in resource overhead.
• Figure 4: Performance and error bars of Full Model vs w/o Node Visibility. In the full version, which includes node-level masking and subgraph sampling, the average error decreases and the bar ranges narrow, indicating improved model stability and performance.
• Figure 5: Analysis of mask ratio. We report the second-best performance from TABLE \ref{['longtermcomparison']} as a reference. Even when 90% of the nodes are masked, we still observe performance gains. During training, both per-epoch time and memory consumption decrease as the mask ratio increases.