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[2602.22274] Positional-aware Spatio-Temporal Network for Large-Scale Traffic Prediction
Summary
The paper presents a Positional-aware Spatio-Temporal Network (PASTN) designed for large-scale traffic prediction, addressing the challenges of spatial and temporal complexities in traffic flow forecasting.
Why It Matters
Traffic flow forecasting is crucial for urban planning and management. The proposed PASTN model enhances predictive accuracy by effectively capturing the spatial and temporal dynamics of traffic data, which is essential for improving traffic management systems and reducing congestion.
Key Takeaways
- PASTN introduces positional-aware embeddings to differentiate node representations.
- The model employs a temporal attention module for improved long-range perception.
- Extensive experiments validate PASTN's effectiveness across various dataset scales.
Computer Science > Machine Learning arXiv:2602.22274 (cs) [Submitted on 25 Feb 2026] Title:Positional-aware Spatio-Temporal Network for Large-Scale Traffic Prediction Authors:Runfei Chen View a PDF of the paper titled Positional-aware Spatio-Temporal Network for Large-Scale Traffic Prediction, by Runfei Chen View PDF HTML (experimental) Abstract:Traffic flow forecasting has emerged as an indispensable mission for daily life, which is required to utilize the spatiotemporal relationship between each location within a time period under a graph structure to predict future flow. However, the large travel demand for broader geographical areas and longer time spans requires models to distinguish each node clearly and possess a holistic view of the history, which has been paid less attention to in prior works. Furthermore, increasing sizes of data hinder the deployment of most models in real application environments. To this end, in this paper, we propose a lightweight Positional-aware Spatio-Temporal Network (PASTN) to effectively capture both temporal and spatial complexities in an end-to-end manner. PASTN introduces positional-aware embeddings to separate each node's representation, while also utilizing a temporal attention module to improve the long-range perception of current models. Extensive experiments verify the effectiveness and efficiency of PASTN across datasets of various scales (county, megalopolis and state). Further analysis demonstrates the efficacy of newly intro...
