Real-Time Delivery Prediction Framework with Spatio-Temporal Fusion and LLM Semantic Enhancement

Food delivery services grew fast during the COVID-19 pandemic. This made it harder to predict rider behavior and delivery time. Many models do not handle the changing and complex nature of this task well. This paper shows STELLAR, a new framework that uses large language models and spatio-temporal learning. STELLAR uses a Qwen-14B-based semantic module to get context from rider data, orders, and regions. A graph attention network captures spatio-temporal patterns. The framework also uses multi-task learning to predict actions and delivery time together. Experiments show STELLAR works better than other models in this setting.