Deep Learning-Based Caution Area Traffic Prediction with Automatic Identification System Sensor Data

Abstract

In a crowded harbor water area, it is a major concern to control ship traffic for assuring safety and maximizing the efficiency of port operations. Vessel Traffic Service (VTS) operators pay much attention to caution areas like ship route intersections or traffic congestion area in which there are some risks of ship collision. They want to control the traffic of the caution area at a proper level to lessen risk. Inertial ship movement makes swift changes in direction and speed difficult. It is hence important to predict future traffic of the caution area earlier on so as to get enough time for control actions on ship movements. In the harbor area, VTS stations collect a large volume of Automatic Identification Service (AIS) sensor data, which contain information about ship movement and ship attributes. This paper proposes a new deep neural network model called Ship Traffic Extraction Network (STENet) to predict the medium-term traffic and long-term traffic of the caution area. The STENet model is trained with AIS sensor data. The STENet model is organized into a hierarchical architecture in which the outputs of the movement and contextual feature extraction modules are concatenated and fed into a prediction module. The movement module extracts the features of overall ship movements with a convolutional neural network. The contextual modules consist of five separated fully-connected neural networks, each of which receives an associated attribute. The separation of feature extraction modules at the front phase helps extract the effective features by preventing unrelated attributes from crosstalking. To evaluate the performance of the proposed model, the developed model is applied to a real AIS sensor dataset, which has been collected over two years at a Korean port called Yeosu. In the experiments, four methods have been compared including two new methods: STENet and VGGNet-based models. For the real AIS sensor dataset, the proposed model has shown 50.65% relative performance improvement on average for the medium-term predictions and 57.65% improvement on average for the long-term predictions over the benchmark method, i.e., the SVR-based method.

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

“…Another significant portion of Set 1' focuses on technical aspects of maritime surveillance, illustrating the constant progress of maritime surveillance systems and the potential for improvement in terms of equipment [55][56][57], data acquisition and fusion [56,58,59], preprocessing and exploitation [60][61][62]. A large share of these papers focusses on event and activity recognition based on trajectory analysis [63,64], sometimes relying on artificial intelligence (deep-learning, data mining) [65,66]. A minor but non-negligible share of papers focuses on uses of maritime surveillance data to serve environmental objectives, either in realtime to ensure compliance with environmental regulations and detect offenders [67][68][69], or in delayed-time to assess impacts on ecosystems and marine and atmospheric pollutions [46,70,71].…”

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

“…Hoque and Sharma applied long short-term memory neural network to forecast ship trajectories, which were employed to suppress the AIS data anomaly [22]. Kim and Lee proposed a novel deep neural network model to remove AIS outliers and thus predict both medium-and long-term ship trajectory variation tendencies [23]. Similar researches can be found in [8,[24][25][26][27][28].…”

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

“…Bannari et al [27] suggested an approach for bathymetric mapping of shallow water of Arabian Gulf near Bahrain using the kriging procedure. Kim and Lee [28] proposed the ship traffic extraction neural network (STENet) to forecast the maritime traffic of the caution area, which represented an increased risk for ships, due to things such as berth limitations. Jeong et al [29] performed multi-criteria route planning using the AIS data while considering waterway depth (in the navigable area) as one of risk factors and criteria for optimization.…”