Excess Delay

Excess delay, encompassing delays exceeding expected or planned durations in various systems, is a significant research area focusing on accurate prediction and mitigation. Current research employs diverse approaches, including machine learning models (like large language models and reinforcement learning) and novel statistical methods (e.g., measure-theoretic embeddings and bootstrap strategies) to address challenges posed by data sparsity, noise, and ambiguous delay characteristics. These efforts aim to improve forecasting accuracy and robustness across applications ranging from transportation networks and resource allocation to anomaly detection in time series data. The ultimate goal is to develop more efficient and reliable systems by better understanding and managing the impact of excess delays.