Abstract
With the increasing application and utility of automatic identification systems (AISs), large volumes of AIS data are collected to record vessel navigation. In recent years, the prediction of vessel trajectories has become one of the hottest research issues. In contrast to existing studies, most researchers have focused on the single-trajectory prediction of vessels. This article proposes a multiple-trajectory prediction model and makes two main contributions. First, we propose a novel method of trajectory feature representation that uses a hierarchical clustering algorithm to analyze and extract the vessel navigation behavior for multiple trajectories. Compared with the classic methods, e.g., Douglas–Peucker (DP) and least-squares cubic spline curve approximation (LCSCA) algorithms, the mean loss of trajectory features extracted by our method is approximately 0.005, and it is reduced by 50% and 30% compared to the DP and LCSCA algorithms, respectively. Second, we design an integrated model for simultaneous prediction of multiple trajectories using the proposed features and employ the long short-term memory (LSTM)-based neural network and recurrent neural network (RNN) to pursue this time series task. Furthermore, the comparative experiments prove that the mean value and standard deviation of root mean squared error (RMSE) using the LSTM are 4% and 14% lower than those using the RNN, respectively.
Highlights
Since 2007, the International Maritime Organization requested that the international vessels over 300 and noninternational vessels over 500 tons must be attached by automatic identification system (AIS) transmitters and receivers [1]. e automatic identification systems (AISs) has become one of the most important broadcast systems of vessel navigational message, e.g., longitude, latitude, course, and speed via VHF. e message of the AIS can be transmitted from ship to shorebased center but can be exchanged between ships.erefore, the AIS has been of great support for marine traffic tracking and management such as collision avoidance and risk evaluation [2,3,4]
We found that the prediction models using both the long short-term memory (LSTM) and recurrent neural network (RNN) can obtain the significantly low root mean squared error (RMSE) when the dimension of the proposed feature is set to 13
A prediction model for multiple trajectories was demonstrated based on a novel trajectory representation method. e proposed method used a hierarchical clustering approach to extract the featured point from the original trajectory based on AIS data, and the common features of multiple trajectories can be obtained by a series of simplification line segments
Summary
Since 2007, the International Maritime Organization requested that the international vessels over 300 and noninternational vessels over 500 tons must be attached by automatic identification system (AIS) transmitters and receivers [1]. e AIS has become one of the most important broadcast systems of vessel navigational message, e.g., longitude, latitude, course, and speed via VHF. e message of the AIS can be transmitted from ship to shorebased center but can be exchanged between ships.erefore, the AIS has been of great support for marine traffic tracking and management such as collision avoidance and risk evaluation [2,3,4]. The vessel trajectory information-based AIS data play a crucial role in the field of transportation studies for significant guidance of navigation behavior analyses [5,6,7,8,9]. The studies briefly used AIS data for navigation behavior analysis and classification, and several classification methods such as the Journal of Advanced Transportation k-nearest neighbor (KNN) algorithm and other new methods were used to classify the trajectory [5, 6]. Scholars mostly focused on trajectory reconstruction- and prediction-based AIS data using regression analysis methods [8,9,10]. Among the above research studies, trajectory prediction is the most key technique of vessel intelligent navigation systems, which can provide navigation guidance and support for vessel operators based on historical AIS data. The existing research studies mainly pursued one single trajectory prediction so that the model is lack of generalization ability, and a training process is required for each trajectory individually [5,6,7,8,9,10,11]
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