Destination Prediction

Destination prediction aims to forecast where individuals or vehicles will travel next, leveraging historical data and contextual information to improve accuracy. Current research emphasizes incorporating spatio-temporal context, using advanced architectures like Long Short-Term Memory (LSTM) networks and Transformers, and addressing the challenges of short-term versus long-term prediction through techniques like progressive pretext task learning. This field is crucial for optimizing transportation systems, personalizing recommendations, and enhancing resource allocation in various sectors, from ride-sharing to urban planning. Improved prediction accuracy relies on effectively integrating diverse data sources and modeling complex individual and collective movement patterns.