A multi-task analysis and modelling paradigm using LSTM for multi-source monitoring data of inland vessels

The vessel monitoring data provide important information for people to understand the vessel dynamic status in real time and make appropriate decisions in vessel management and operations. However, some of the essential data may be incomplete or unavailable. In order to recover or predict the missing information and best exploit the vessels monitoring data, this paper combines statistical analysis, data mining and neural network methods to propose a multi-task analysis and modelling framework for multi-source monitoring data of inland vessels. Specifically, an advanced neural network, Long Short-Term Memory (LSTM) was tailored and employed to tackle three important tasks, including vessel trajectory repair, engine speed modelling and fuel consumption prediction. The developed models have been validated using the real-life vessel monitoring data and shown to outperform some other widely used modelling methods. In addition, statistics and data technologies were employed for data extraction, classification and cleaning, and an algorithm was designed for identification of the vessel navigational state

A Practical Estimation Method of Inland Ship Speed Under Complex and Changeful Navigation Environment

The complexity and changefulness of inland navigation environment in space and time makes it hard to guarantee the applicability and accuracy of existing ship speed models. In this paper, a novel method for inland ship speed modelling under complex and changeful navigation environment is proposed. Firstly, an unsupervised machine learning algorithm, Density-Based Spatial Clustering of Application with Noise (DBSCAN), is utilized to cluster the environmental data including water level, water speed, wind speed and wind direction, to get the segment division information, which greatly helps reduce the influence of other uncertain environmental factors on the speed model. Then, Generalized Regression Neural Network (GRNN) is tailored and employed to build the ship speed estimation model with multiple input variables. Finally, a detailed case study of a ship sailing in the Yangtze River trunk line is conducted to validate the proposed methods. The results show that the ship speed model established based on machine learning methods works effectively in speed estimation and analysis. Moreover, compared with other regression methods and neural networks, the proposed GRNN model has the best performance in ship speed modelling

Fitting Analysis of Inland Ship Fuel Consumption Considering Navigation Status and Environmental Factors

The strategy of ecological priority and green development in China has made the fuel consumption of inland ships receive unprecedented attentions. Reliable fuel consumption prediction is the vital basis of navigation planning, energy supervision, and efficiency optimization. In this article, a cargo ship sailing on the Yangtze River trunk line was taken as the research object. A comprehensive fitting analysis of inland ship fuel consumption was conducted, and a prediction method was proposed. First, the multi-source data including ship navigation status and environment information were collected by multi-source sensors. Second, to conduct a detailed analysis of the collected data, the authors proposed data pre-processing and trajectory segmentation methods and analyzed the correlation between multi-source variables and fuel consumption. Third, a Back Propagation Neural Network with double hidden layers (DBPNN) was tailored to build a fuel consumption prediction model. Fourth, the developed model was validated using real ship measurement data. Different input variables were selected for fuel consumption prediction, and the results showed that after adding the variables of environmental feature including water level, water speed, wind speed, wind angle, and route segment, the prediction error RMSE (root mean square error) and MAE (mean absolute error) were reduced by 35.31% and 30.30%, respectively, while the R2 (R-squared) increased to 0.9843. What’s more, compared with other ANNs (artificial neural networks) such as Elman, RBF (radial basis function), three support vector regression (SVR) models, random forest regression (RFR) model, GRNN (generalized regression neural network), RNN (recurrent neural network), GRU (gated recurrent unit) and LSTM (long short-term memory) the proposed DBPNN model showed better performance in fuel consumption prediction

Modeling Categorized Truck Arrivals at Ports: Big Data for Traffic Prediction

Accurate truck arrival prediction is complex but critical for container terminals. A deep learning model combining Gated Recurrent Unit (GRU) and Fully Connected Neural Network (FCNN), is proposed to predict daily truck arrivals using fusion technology. The model can efficiently analyze sequence and cross-section data sets. The new feature in the new model lies in that it, for the first time, incorporates the new parameters influencing traffic volumes such as the vessel-related information, arrival weekdays, and weather conditions into the long-time series of truck arrivals. Furthermore, truck arrivals are predicted in three groups based on their movement purposes: pick-up, delivery, and dual. it also contributes to the literature in a sense that the performance of the model is tested using real big data from a world-leading container port in Southern China. The results generate insightful managerial implications for guiding port traffic management in a generic manner. It reveals the relation of export container arrivals with the Container Yard (CY) closing time of a specific vessel. It is demonstrated the proposed model outperforms the currently available methods with an improved accuracy rate of prediction by 23.44% (dual), 32.09% (pick-up), and 26.99% (delivery), respectively. As a result, the model can better reflect reality compared to the existing ones in the literature. It is also evident that the 3-categorized prediction model can significantly help increase prediction accuracy in comparison with the 2-categorized methods used in practice

AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods

Maritime transport faces new safety challenges in an increasingly complex traffic environment caused by large-scale and high-speed ships, particularly with the introduction of intelligent and autonomous ships. It is evident that Automatic Identification System (AIS) data-driven ship trajectory prediction can effectively aid in identifying abnormal ship behaviours and reducing maritime risks such as collision, stranding, and contact. Furthermore, trajectory prediction is widely recognised as one of the critical technologies for realising safe autonomous navigation. The prediction methods and their performance are the key factors for future safe and automatic shipping. Currently, ship trajectory prediction lacks the real performance measurement and analysis of different algorithms, including classical machine learning and emerging deep learning methods. This paper aims to systematically analyse the performance of ship trajectory prediction methods and pioneer experimental tests to reveal their advantages and disadvantages as well as fitness in different scenarios involving complicated systems. To do so, five machine learning methods (i.e., Kalman Filter (KF), Support Vector Progression (SVR), Back Propagation network (BP), Gaussian Process Regression (GPR), and Random Forest (RF)) and seven deep learning methods (i.e., Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (Bi-LSTM), Sequence to Sequence (Seq2seq), Bi-directional Gate Recurrent Unit (Bi-GRU), and Transformer) are first extracted from the state-of-the-art literature review and then employed to implement the trajectory prediction and compare their prediction performance in the real world. Three AIS datasets are collected from the waters of representative traffic features, including a normal channel (i.e., the Chengshan Jiao Promontory), complex traffic (i.e., the Zhoushan Archipelago), and a port area (i.e., Caofeidian port). They are selected to test and analyse the performance of all twelve methods based on six evaluation indexes and explore the characteristics and effectiveness of the twelve trajectory prediction methods in detail. The experimental results provide a novel perspective, comparison, and benchmark for ship trajectory prediction research, which not only demonstrates the fitness of each method in different maritime traffic scenarios, but also makes significant contributions to maritime safety and autonomous shipping development

Identifying the Causes of Ship Collisions Accident Using Text Mining and Bayesian Networks

Under the backdrop of the robust growth of the global economy, the water transport industry is experiencing rapid development, resulting in an increase in ship collisions and a critical water traffic safety situation. This study uses text mining techniques to gather a corpus of data. The corpus includes human factors, ship factors, natural environmental factors, and management factors, which are used as target data to obtain a high-dimensional sparse original feature vector space set comprising eigenvalues and eigenvalue weight attributes. Chi-square statistics are utilised to reduce dimensionality, resulting in a final set of 33-dimensional text feature items that determine the causal factors of ship collision risk. Taking the four steps involved in the collision process as the primary focus, a Bayesian network structure for ship collision risk is constructed based on the “human-ship-environment-management” system. By incorporating existing ship collision accident/danger reports, conditional probability tables are computed for each node in the Bayesian network structure, enabling the modelling of ship collision risk. The model is validated through an example, revealing that, under relevant conditions, the probability of collision exceeds 90 %. This finding demonstrates the validity of the model and allows one to deduce the primary cause of ship collision accidents

Reliable Navigational Scene Perception for Autonomous Ships in Maritime Environment

Due to significant advances in robotics and transportation, research on autonomous ships has attracted considerable attention. The most critical task is to make the ships capable of accurately, reliably, and intelligently detecting their surroundings to achieve high levels of autonomy. Three deep learning-based models are constructed in this thesis to perform complex perceptual tasks such as identifying ships, analysing encounter situations, and recognising water surface objects. In this thesis, sensors, including the Automatic Identification System (AIS) and cameras, provide critical information for scene perception. Specifically, the AIS enables mid-range and long-range detection, assisting the decision-making system to take suitable and decisive action. A Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) is used to detect ships or objects. Following that, a Semi- Supervised Convolutional Encoder-Decoder Network (SCEDN) is developed to classify ship encounter situations and make a collision avoidance plan for the moving ships or objects. Additionally, cameras are used to detect short-range objects, a supplementary solution to ships or objects not equipped with an AIS. A Water Obstacle Detection Network based on Image Segmentation (WODIS) is developed to find potential threat targets. A series of quantifiable experiments have demonstrated that these models can provide reliable scene perception for autonomous ships

Machine Learning for Enhanced Maritime Situation Awareness: Leveraging Historical AIS Data for Ship Trajectory Prediction

In this thesis, methods to support high level situation awareness in ship navigators through appropriate automation are investigated. Situation awareness relates to the perception of the environment (level 1), comprehension of the situation (level 2), and projection of future dynamics (level 3). Ship navigators likely conduct mental simulations of future ship traffic (level 3 projections), that facilitate proactive collision avoidance actions. Such actions may include minor speed and/or heading alterations that can prevent future close-encounter situations from arising, enhancing the overall safety of maritime operations. Currently, there is limited automation support for level 3 projections, where the most common approaches utilize linear predictions based on constant speed and course values. Such approaches, however, are not capable of predicting more complex ship behavior. Ship navigators likely facilitate such predictions by developing models for level 3 situation awareness through experience. It is, therefore, suggested in this thesis to develop methods that emulate the development of high level human situation awareness. This is facilitated by leveraging machine learning, where navigational experience is artificially represented by historical AIS data. First, methods are developed to emulate human situation awareness by developing categorization functions. In this manner, historical ship behavior is categorized to reflect distinct patterns. To facilitate this, machine learning is leveraged to generate meaningful representations of historical AIS trajectories, and discover clusters of specific behavior. Second, methods are developed to facilitate pattern matching of an observed trajectory segment to clusters of historical ship behavior. Finally, the research in this thesis presents methods to predict future ship behavior with respect to a given cluster. Such predictions are, furthermore, on a scale intended to support proactive collision avoidance actions. Two main approaches are used to facilitate these functions. The first utilizes eigendecomposition-based approaches via locally extracted AIS trajectory segments. Anomaly detection is also facilitated via this approach in support of the outlined functions. The second utilizes deep learning-based approaches applied to regionally extracted trajectories. Both approaches are found to be successful in discovering clusters of specific ship behavior in relevant data sets, classifying a trajectory segment to a given cluster or clusters, as well as predicting the future behavior. Furthermore, the local ship behavior techniques can be trained to facilitate live predictions. The deep learning-based techniques, however, require significantly more training time. These models will, therefore, need to be pre-trained. Once trained, however, the deep learning models will facilitate almost instantaneous predictions