Deep learning-based prediction of ship transit time

Vessel traffic system (VTS) operators instruct ships to wait for entry and departure to sail one-way in order to prevent ship collision accidents in harbors with narrow routes. At present, these instructions are not based on scientific or statistical data. Consequently, there was a significant deviation depending on the individual capabilities of the VTS operators. Accordingly, in this study, a 1D convolutional neural network model was built by collecting ship and weather data to predict the exact travel time for ship arrival/departure waiting for instructions at the harbor. The proposed deep learning model was confirmed to be improved by more than 5.9% compared to other ensemble machine learning models. Through this study, it is possible to predict the time required to enter and depart a vessel in various situations; therefore, the VTS operators are expected to assist in providing accurate information to the vessel and determining the waiting order.