Transfer Learning for Cross-City Traffic Prediction to Solve Data Scarcity

Deep learning models have demonstrated significant achievements in traffic prediction. However, their predictive performance substantially declines when faced with the scarcity of urban traffic data. Addressing the challenges of data scarcity and heterogeneity between cities, cross-city transfer learning has emerged as a promising solution. This paper proposes a domain adaptation cross-city model, which integrates traffic data with auxiliary urban data for domain adaptation in cross-city transfer learning. Specifically, we designed a domain fusion module to measure the differences between cities. Firstly, the knowledge extractor within the domain fusion module learns the knowledge from urban auxiliary data, such as road networks and points of interest, and calculates transferable knowledge. Then, dynamic time warping is used to measure the similarity of traffic time series. By combining these two aspects, we derive the domain differences between cities. Finally, the spatiotemporal network undergoes pre-learning using abundant data from the source city. According to the differences between cities, the model is fine-tuned to improve the adaptability of the model to the target domain. Experimental results on real-world data validate the effectiveness of the proposed model.