Regional Knowledge Transfer for Urban Traffic Flow Prediction via Satellite Imagery Assisted Contrastive Domain Adaptation

In traffic flow prediction, the efficacy of deep learning models is largely contingent upon the availability of extensive training datasets, presenting a formidable challenge in data-scarce environments. Transfer learning has emerged as a promising strategy to address this challenge by leveraging abundant data from source cities to enhance predictive accuracy in target cities with limited data. Nonetheless, existing methods frequently neglect the distinct characteristics and interrelationships among various regions within cities, leading to predominantly city-level knowledge transfers that underutilize the potential of transferred information. In this paper, we present SERT, a fine-grained regional knowledge transfer method specifically designed to mitigate data scarcity in traffic flow prediction. SERT initiates the process by establishing relationships between source and target regions through the integration of satellite imagery and Points of Interest (POI) data, effectively capturing region-specific features to create matched region pairs. Subsequently, we propose an innovative contrastive domain adaptation strategy to align the features of these matched regions, thereby facilitating inter-regional knowledge transfer while maximizing the feature distance of unmatched regions to reduce interference from irrelevant data. This approach enables the effective transfer of valuable knowledge from the source cities to its relevant counterparts in the target city. Comprehensive experimental results demonstrate that SERT outperforms existing methods in terms of prediction accuracy while ensuring significant computational efficiency. The code is available at https://github.com/MobiXg/SERT