Trajectory Representation Learning

Trajectory representation learning aims to convert raw movement data into meaningful, low-dimensional representations suitable for various downstream tasks like traffic prediction and route optimization. Current research emphasizes self-supervised learning approaches, often employing graph neural networks or transformer architectures, to capture complex spatio-temporal relationships and mitigate the influence of environmental confounders through causal inference techniques. These advancements improve the accuracy and efficiency of trajectory analysis, impacting fields such as autonomous driving, urban planning, and location-based services by enabling more robust and insightful modeling of human and vehicle movement.