Система визуальной аналитики для объяснения и улучшения моделей прогнозирования дорожного движения на основе механизма вниманияA visual analytics system for explaining and improving attention-based traffic forecasting models

This thesis explores the intersection of human-AI interaction and transportation forecasting, focusing on the development and application of attention-based neural network models, particularly memory networks. The research investigates the challenges inherent in traffic prediction tasks, such as capturing complex spatio-temporal dependencies and providing interpretable predictions. Leveraging the attention mechanism, the proposed models aim to enhance prediction accuracy and provide insights into the underlying factors influencing traffic dynamics. The thesis encompasses a comprehensive analysis of spatio-temporal data, model development, and evaluation, as well as techniques for visualizing and interpreting model predictions. Through empirical studies and case examples, the effectiveness and practical implications of the proposed approach are demonstrated. The findings contribute to advancing the understanding of human-AI interaction in transportation systems and provide valuable insights for improving the accuracy and interpretability of predictive models in complex real-world scenarios.