Sequence-aware adaptive graph convolutional recurrent networks for traffic forecasting

Abstract

Traffic forecasting is a crucial task for the Intelligent Transportation System (ITS). A promising research direction for improving traffic prediction is to learn dynamic graph structures incorporating the hidden dependencies from the training sequence data. However, existing works optimize these dynamic graph structures only for the training data, regarding them as static when testing with new input sequences. This constrains the forecasting model’s ability to address potential discrepancies between training and testing sequences, which may arise from unforeseen changes in the traffic environment. To address this challenge, we propose a new encoder-decoder framework for traffic forecasting, Sequence-aware Adaptive Graph Convolutional Recurrent Networks (SAGCRN). The encoder augments an input sequence by exploiting spatio-temporal contexts and traffic pattern storage. Then, the decoder adaptively learns a new graph structure reflecting the augmented input sequence and uses it for prediction. To further enhance the sequence-specialized graph structure, SAGCRN optimizes the stored traffic patterns to be more discriminative. We demonstrate the superior performance of SAGCRN on three real-world benchmark datasets, comparing it with nine baseline models. The additional sensitivity and qualitative analyses substantiate the effectiveness of our model. For reproducibility, the source code is available at https://github.com/gooriiie/SAGCRN.