The field of trajectory prediction and logistics management is moving towards more advanced and nuanced modeling of complex systems. Researchers are developing new methods to capture high-order interactions and adaptively model both explicit one-hop interactions and implicit high-order dependencies. This is being achieved through the use of graph neural networks, virtual graphs, and expert routers. Additionally, there is a growing focus on improving the efficiency and sustainability of logistics and supply chain management, with a emphasis on reducing air pollutant emissions and predicting shipment types and logistics delays. Noteworthy papers in this area include: Reverberation, which proposes a new reverberation transform and model to simulate and predict different latency preferences of each agent. ViTE, which introduces a novel framework for pedestrian trajectory prediction using a virtual graph and expert router. Enhancing Regional Airbnb Trend Forecasting, which proposes a novel time-series forecasting framework to predict key Airbnb indicators using LLM-based embeddings of accessibility and human mobility.