Selective Cross-City Transfer Learning for Traffic Prediction via Source City Region Re-Weighting

Abstract

Deep learning models have been demonstrated powerful in modeling complex spatio-temporal data for traffic prediction. In practice, effective deep traffic prediction models rely on large-scale traffic data, which is not always available in real-world scenarios. To alleviate the data scarcity issue, a promising way is to use cross-city transfer learning methods to fine-tune well-trained models from source cities with abundant data. However, existing approaches overlook the divergence between source and target cities, and thus, the trained model from source cities may contain noise or even harmful source knowledge. To address the problem, we propose CrossTReS, a selective transfer learning framework for traffic prediction that adaptively re-weights source regions to assist target fine-tuning. As a general framework for fine-tuning-based cross-city transfer learning, CrossTReS consists of a feature network, a weighting network, and a prediction model. We train the feature network with node- and edge-level domain adaptation techniques to learn generalizable spatial features for both source and target cities. We further train the weighting network via source-target joint meta-learning such that source regions helpful to target fine-tuning are assigned high weights. Finally, the prediction model is selectively trained on the source city with the learned weights to initialize target fine-tuning. We evaluate CrossTReS using real-world taxi and bike data, where under the same settings, CrossTReS outperforms state-of-the-art baselines by up to 8%. Moreover, the learned region weights offer interpretable visualization.