Ship trajectory prediction for intelligent traffic management using clustering and ANN

Gan, Shaojun; Liang, Shan; Kang, Li; Deng, Jiankang; Cheng, Tingli

doi:10.1109/control.2016.7737569

Cited by 37 publications

(22 citation statements)

References 6 publications

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“…There are various works for maritime traffic prediction [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Perera et al [ 12 ] proposed both a neural network-based method that detects and tracks multiple ships by using the radar data collected on the shore-side station and a Kalman filter-based method that predicts ship trajectories from current ship data.…”

Section: Related Workmentioning

confidence: 99%

“…There are several neural network-based traffic prediction models that use both ship trajectory data and other traffic-related factors. Gan et al [ 20 ] proposed a ship traffic estimation method for narrow water passage. The method first trains a neural network model with a hidden layer, which determines clusters of ship trajectory data along with ship’s speed, loading capacity, weight, maximum power and water level.…”

Section: Related Workmentioning

confidence: 99%

“…Various maritime traffic prediction methods have been developed [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Some methods are based on mathematical modeling [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…It is difficult to develop satisfactory mathematical model-based traffic prediction because the traffic is not just governed by physical laws, but also by various regulations enforced in the harbor sea area. There are various machine learning-based prediction methods [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Deep neural networks are a powerful tool to attack some difficult problems like traffic prediction in a caution area.…”

Section: Introductionmentioning

confidence: 99%

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

Kim

Lee

2018

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Various maritime traffic prediction methods have been developed [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Some methods are based on mathematical modeling [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kim

Lee

2018

Sensors

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“…Vaidehi et al [10] used an ordinary Kalman filter with neural network elements and an auxiliary Kalman filtering scheme to track highly maneuverable multi-targets. Gan et al [11] grouped historical trajectories by the k-means algorithm and then used artificial neural network (ANN) models to predict ship trajectories. Cai and Zhang [12] constructed an offline model using a hybrid particle swarm optimization evolutionary algorithm (PSO-EA) for time series prediction.…”

Section: Introductionmentioning

confidence: 99%

Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

Gao

Shi

2018

Sensors

101

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The real-time prediction of ship behavior plays an important role in navigation and intelligent collision avoidance systems. This study developed an online real-time ship behavior prediction model by constructing a bidirectional long short-term memory recurrent neural network (BI-LSTM-RNN) that is suitable for automatic identification system (AIS) date and time sequential characteristics, and for online parameter adjustment. The bidirectional structure enhanced the relevance between historical and future data, thus improving the prediction accuracy. Through the “forget gate” of the long short-term memory (LSTM) unit, the common behavioral patterns were remembered and unique behaviors were forgotten, improving the universality of the model. The BI-LSTM-RNN was trained using 2015 AIS data from Tianjin Port waters. The results indicate that the BI-LSTM-RNN effectively predicted the navigational behaviors of ships. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could potentially be applied as the predictive foundation for various intelligent systems, including intelligent collision avoidance, vessel route planning, operational efficiency estimation, and anomaly detection systems.

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Maritime Data Processing in Relational Databases

Emmanuel¹,

Ray²,

Camossi³

et al. 2021

Guide to Maritime Informatics

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Maritime data processing research has long used spatio-temporal relational databases. This model suits well the requirements of off-line applications dealing with average-size and known in advance geographic data that can be represented in tabular form. This chapter explores off-line maritime data processing in such relational databases and provides a step-by-step guide to build a maritime database for investigating maritime traffic and vessel behaviour. Along the chapter, examples and exercises are proposed to build a maritime database using the data available in the open, heterogeneous, integrated dataset for maritime intelligence, surveillance, and reconnaissance that is described in [41]. The dataset exemplifies the variety of data that are nowadays available for monitoring the activities at sea, mainly the Automatic Identification System (AIS), which is openly broadcast and provides worldwide information on the maritime traffic. All the examples and the exercises refer to the syntax of the widespread relational database management system PostgreSQL and its spatial extension PostGIS, which are an established and standard-based combination for spatial data representation and querying. Along the chapter, the reader is guided to experience the spatio-temporal features offered by the database management system, including spatial and temporal data types, indexes, queries and functions, to incrementally investigate vessel behaviours and the resulting maritime traffic.

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Ship trajectory prediction for intelligent traffic management using clustering and ANN

Cited by 37 publications

References 6 publications

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

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

Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

Maritime Data Processing in Relational Databases

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