Ship Trajectory Prediction Based on Attention in Bidirectional Recurrent Neural Networks
Chao Wang, Yuhui Fu
20202020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)20 citationsDOI
Abstract
Using AIS data to further improve the accuracy of ship trajectory prediction, a model based on Attention in Bidirectional Long Short- Term Memory Recurrent Neural Networks (BLSTM) is proposed. The model learns from AIS data in a certain area over a while. Final model performance comparing the learning results of the four Recurrent Neural Network models on the same data set, let them make track predictions on the same AIS data, and proved that the model has higher prediction accuracy. The prediction results can provide a reference for ship traffic organization and management in the detection of abnormal ship behavior, early warning of ship collision or grounding, etc.
Topics & Concepts
Computer scienceTrajectoryArtificial neural networkRecurrent neural networkTrack (disk drive)Artificial intelligenceLong short term memorySet (abstract data type)Data modelingTraining setData setCollisionMachine learningData miningComputer securityDatabaseProgramming languageOperating systemAstronomyPhysicsMaritime Navigation and SafetyTarget Tracking and Data Fusion in Sensor NetworksTraffic Prediction and Management Techniques