Methods of ship trajectory data processing for applying artificial neural network in port area - II Kim, Kwang; Myung Lee, Keon; Young Ahn, Jang (International Journal of Engineering and Technology, 2018)
Background/Objectives: In Vessel Traffic Service (VTS), prediction of the flow of vessel traffic is essential to serve safety information and control ship traffic. However, it is difficult to predict a shipâ€™s speed due to many external forces and environmental conditions. This study proposes a data processing method to convert ship speed data to categorical data by dividing ship navigating routes into several gate lines.Methods/Statistical analysis: A shipâ€™s trajectory is converted to each routeâ€™s gate line speed. To determine the gate line speed, we convertedthe previous and subsequent gate line speeds into category data. The input and output category data were applied to a multilayer perceptron network using as input variablesthe previous speed variance category, ship type, and ship length, and as output variable the subsequent speed variance.Findings: These results are useful because categorical data can be applied to various neural network models. As a result of the conducted experiments, the accuracy of the model improved when many gate lines are included.Improvements/Applications: The study results can be applied topredict ship traffic flow for VTS operators.Â