A Retrieval Augmented Spatio-Temporal Framework for Traffic Prediction

• URL: http://arxiv.org/abs/2508.16623v1
• Date: Thu, 14 Aug 2025 10:11:39 GMT
• Title: A Retrieval Augmented Spatio-Temporal Framework for Traffic Prediction
• Abstract summary: RAST achieves superior performance while maintaining efficiency in large-scale datasets.<n>Our framework consists of three key designs: 1) Decoupled and Query Retriever to capture decoupled temporal features and construct residual fusion via Retrieval-Augmented Generation (RAG); 2) Universal Backbone Predict Storeor that accommodates pre-trained ST-GNNs or simple predictors; and 3) Universal Backbone Predict Storeor that accommodates pre-trained ST-GNNs or simple predictors.
• Score: 33.28893562327803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traffic prediction is a cornerstone of modern intelligent transportation systems and a critical task in spatio-temporal forecasting. Although advanced Spatio-temporal Graph Neural Networks (STGNNs) and pre-trained models have achieved significant progress in traffic prediction, two key challenges remain: (i) limited contextual capacity when modeling complex spatio-temporal dependencies, and (ii) low predictability at fine-grained spatio-temporal points due to heterogeneous patterns. Inspired by Retrieval-Augmented Generation (RAG), we propose RAST, a universal framework that integrates retrieval-augmented mechanisms with spatio-temporal modeling to address these challenges. Our framework consists of three key designs: 1) Decoupled Encoder and Query Generator to capture decoupled spatial and temporal features and construct a fusion query via residual fusion; 2) Spatio-temporal Retrieval Store and Retrievers to maintain and retrieve vectorized fine-grained patterns; and 3) Universal Backbone Predictor that flexibly accommodates pre-trained STGNNs or simple MLP predictors. Extensive experiments on six real-world traffic networks, including large-scale datasets, demonstrate that RAST achieves superior performance while maintaining computational efficiency.

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