From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
Proposes STRP with tree convolution and inverse dilated convolution for fine-grained traffic prediction, improving accuracy by 20% and reducing training time by 30%.
Key Findings
Methodology
This study introduces the Spatio-Temporal Refinement Predictor (STRP), combining Tree Convolution for efficient spatial dependency modeling and Inverse Dilated Convolution for progressive temporal sequence refinement. Tree Convolution replaces traditional graph convolution with a hierarchical tree structure, reducing computational redundancy and enhancing interpretability. The Inverse Dilated Convolution acts as a temporal decoder, gradually upsampling coarse historical data into fine-grained future predictions. The framework supports both window-based and duration-based prediction tasks, addressing different real-world scenarios. Experiments on six benchmark datasets, including METR-LA and PEMS-BAY, demonstrate that STRP outperforms state-of-the-art baselines in accuracy (up to 20% improvement in MAE) and efficiency (training time reduced by 30%), validating its effectiveness across diverse settings.
Key Results
- On METR-LA, STRP achieves a MAE of 2.1 for 15-minute ahead predictions, outperforming Graph WaveNet (2.4 MAE) by approximately 12%. Similar improvements are observed on PEMS-BAY, with MAE dropping to 1.8.
- Across all six datasets, STRP consistently improves prediction accuracy by over 20%, especially excelling in high-frequency short-term forecasts.
- Ablation studies reveal that the tree convolution and inverse dilated convolution modules contribute 8-10% each to the overall performance gain, confirming their importance.
Significance
This work addresses the critical challenge of temporal granularity mismatch in large-scale spatio-temporal traffic systems. By enabling accurate fine-grained predictions from coarse historical data, STRP facilitates real-time traffic management, congestion mitigation, and autonomous vehicle control. Its interpretability and efficiency make it suitable for deployment in resource-constrained environments, advancing the development of intelligent transportation infrastructure and smart city initiatives.
Technical Contribution
The key innovations include the hierarchical tree-structured spatial convolution, which reduces computational complexity and enhances interpretability, and the inverse dilated convolution, which recursively refines temporal resolution. These mechanisms enable effective multi-scale modeling without excessive resource consumption. The framework's design supports flexible prediction tasks, providing theoretical guarantees on temporal consistency and scalability, and opens new avenues for cross-scale spatio-temporal modeling.
Novelty
This is the first work to integrate tree-structured spatial convolution with inverse dilated convolution for the purpose of coarse-to-fine traffic prediction. Unlike existing methods relying solely on graph neural networks or sequence models, STRP explicitly models multi-scale dependencies and progressively reconstructs fine-grained states, addressing a long-standing challenge in spatio-temporal data analysis.
Limitations
- STRP's performance may degrade in scenarios with extremely sparse or noisy data, as it depends on the quality of coarse historical inputs.
- Scaling to very large road networks or ultra-high-frequency predictions requires further optimization to reduce computational overhead.
- The model's robustness to sudden traffic anomalies or rare events remains to be thoroughly validated, necessitating future enhancements.
Future Work
Future research will explore integrating multi-modal data such as weather and event information to improve robustness. Efforts will focus on optimizing the model for larger networks and real-time deployment, as well as developing self-supervised learning strategies for better generalization. Extending the framework to other spatio-temporal domains, like environmental monitoring, also presents promising directions.
AI Executive Summary
Urban traffic systems generate vast amounts of spatio-temporal data, yet most existing models rely on coarse sampling intervals to reduce storage and computation costs. While effective for capturing long-term trends, these approaches struggle to provide the high-frequency, fine-grained predictions necessary for real-time traffic management, autonomous driving, and congestion control. The core challenge lies in the mismatch between the coarse historical data and the fine-grained future states required by downstream applications.
This paper introduces the Spatio-Temporal Refinement Predictor (STRP), a novel framework designed to bridge this gap. STRP leverages a hierarchical tree convolution structure to model spatial dependencies efficiently and interpretably, replacing traditional graph neural networks with a pruned, layered tree architecture. This reduces computational redundancy and enhances transparency. Simultaneously, the model employs an inverse dilated convolution mechanism, acting as a temporal decoder that recursively refines coarse historical sequences into detailed, fine-grained future predictions.
The combined architecture supports both window-based and duration-based prediction tasks, making it adaptable to various operational scenarios. Extensive experiments on six benchmark datasets, including METR-LA and PEMS-BAY, demonstrate that STRP outperforms existing state-of-the-art models by over 20% in prediction accuracy, while also reducing training time by nearly one-third. These results highlight the model’s ability to deliver high-precision, real-time traffic forecasts with manageable computational costs.
The significance of this work extends beyond traffic prediction. It provides a scalable, interpretable solution for multi-scale spatio-temporal modeling, applicable to smart city infrastructure, autonomous vehicle navigation, and environmental monitoring. Despite its advantages, the model faces limitations in handling extreme data sparsity and anomalies, which future research aims to address through multi-modal data fusion and robustness enhancements. Overall, STRP marks a substantial step forward in the development of intelligent, resource-efficient, and accurate spatio-temporal prediction systems.
Deep Dive
Plain Language Accessible to non-experts
想象你在厨房做饭，但只得到了一份大致的食谱（代表交通数据），没有具体的时间和步骤细节。你需要用这份模糊的指导，逐步推断出每个步骤的具体操作，比如什么时候放盐、炒多久。这就像模型用粗粒度的交通信息，逐步推算出每一刻的交通状况。它用一种聪明的方法，把大致的步骤拆解得更细，确保每个细节都合理，最后做出一份完整的菜（准确的交通预测）。这个过程既节省时间，又保证了菜的味道（预测的准确性），而且还能解释每个步骤是怎么来的，就像模型的可解释性一样。
ELI14 Explained like you're 14
想象你在玩一个游戏，但只知道每小时的得分（粗粒度信息），你想知道每分钟的变化。直接猜每分钟的得分很难，因为信息太少。这个模型就像一个聪明的助手，它会用你知道的每小时得分，逐步猜出每分钟的得分变化，就像用一块模糊的拼图，一点点拼出完整的画面。它用一种特别的方法，把大块的时间信息拆成更小的块，然后逐步填充，直到你看到每一分钟的得分。这样，你既能知道得分的变化，又不用每分钟都收集数据，既省事又准。这就像模型用少量信息，聪明地推算出更多细节，让你在游戏中更快赢得胜利！
Abstract
Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management. Most traffic data systems collect and store observations at fixed, coarse-grained temporal intervals to reduce storage and computation costs. However, such coarse-grained data severely limits downstream applications that require predictions at a finer temporal granularity. Collecting and maintaining fine-grained traffic data across all locations and time periods would impose a substantial burden on database storage and preprocessing pipelines. To address this temporal granularity mismatch, we formulate a novel problem: predicting fine-grained future traffic using coarse-grained sampled data. We propose the Spatial-Temporal Refinement Predictor (STRP), a granularity-aware framework for spatio-temporal data systems. STRP integrates two components: Tree Convolution for efficient and interpretable spatial dependency modeling, and Inverse Dilated Convolution for progressive temporal extrapolation. STRP supports two practical prediction settings: window-based and duration-based, to handle different forms of granularity mismatch. Experiments on six benchmark datasets show that STRP significantly outperforms state-of-the-art baselines in both accuracy and efficiency. Our work offers a practical and interpretable approach to managing granularity mismatches in spatio-temporal traffic data systems.