Trajectory Visualization

Trajectory visualization focuses on representing and interpreting movement data across diverse fields, aiming to enhance understanding of complex systems and processes. Current research emphasizes developing interactive and explainable visualizations, often integrating techniques like LIME, SHAP, and attention mechanisms to provide insights into model behavior and decision-making, particularly within the context of reinforcement learning and mobility data science. These advancements improve the interpretability of complex models and facilitate communication between experts and non-experts, ultimately leading to more effective analysis and informed decision-making in various scientific and engineering domains.