Trend Similarity-Enhanced Holiday Intercity Mobility Flow Prediction Under Historical Data Scarcity

Abstract

Intercity mobility management plays a critical role in promoting urban sustainability and regional integration. Accurate prediction of mobility flows is essential for optimizing transport operations and enhancing resource allocation, especially during holidays. However, event-induced scarcity of reliable historical data makes it challenging for existing methods to capture the abrupt changes in holiday mobility patterns. To address these challenges, this article proposes the dynamic time-space holiday feature captor (DHC), which integrates spatiotemporal correlations in time-series data with cross-holiday similarities to effectively model the dynamic patterns of holiday mobility, thereby significantly improving prediction accuracy. The DHC model comprises four key modules: 1) vision dynamic extension (VDE); 2) spatiotemporal feature acquisition (SFA); 3) vision feature extraction (VFE); and 4) holiday feature fusion (HFF). The main experimental results show that DHC outperforms existing methods on multiple metrics, achieving reductions of 33.98%, 26.14%, and 35.75% in MAE, RMSE, and MAPE, respectively. Moreover, the model’s effectiveness and generalizability are further validated through ablation studies and additional experiments conducted across various holidays. This research advances mobility-flow prediction and management during holidays and contributes to the development of sustainable transportation systems.