Departure Time Pattern

Departure time pattern research focuses on understanding and predicting when entities, ranging from ships and trucks to buses and airplanes, begin their journeys. Current research employs diverse approaches, including machine learning models like Temporal Convolutional Networks and Transformers, often incorporating multiple data sources (e.g., weather, booking information, traffic data) to improve prediction accuracy. These studies aim to optimize resource allocation, improve service reliability (e.g., reducing delays), and enhance situational awareness in various transportation sectors, ultimately leading to more efficient and effective operations.