Tag
This paper presents a framework for integrating semantic knowledge from general-purpose knowledge graphs into spatio-temporal traffic forecasting models to improve prediction accuracy and interpretability by providing contextual information beyond physical connectivity.
DSETA is a dual-stage continual learning framework for travel time prediction that separates intra-day real-time adaptation from inter-day long-term trend learning, with online A/B tests showing MAE reductions across three cities and successful deployment in DiDi's production environment.
This survey reviews neural architecture search (NAS) methods applied to traffic prediction, organizing them by search strategy (gradient-based, evolutionary, one-shot weight-sharing) and discussing challenges such as computational scalability, cross-city generalization, and future directions.