Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times

ArXi:2604.06251v1 Announce Type: cross This article presents the results of a data science study conducted at a container terminal, aimed at reducing unproductive container moves through the prediction of service requirements and container dwell times. We develop and evaluate machine learning models that leverage historical operational data to anticipate which containers will require pre-clearance handling services prior to cargo release and to estimate how long they are expected to remain in the terminal.