A detailed exhaust emission inventory of ships by using Automatic Identification System (AIS) data was developed for Tianjin Port, one of the top 10 world container ports and the largest port in ...North China. It was found that in 2014, ship emissions are 2.93 × 104, 4.13 × 104, 4.03 × 103, 3.72 × 103, 1.72 × 103 and 3.57 × 103 tonnes of SO2, NOx, PM10, PM2.5, NMVOC and CO respectively, which are equivalent to 11.07%, 9.40%, 2.43%, 3.10%, 0.43% and 0.16% respectively of the non-ship anthropogenic totals in Tianjin. The total CO2 emissions is approximately 1.97 × 106 tonnes. The container ships and dry bulk cargo ships contributed about 70% of the total ship emissions of NOx, SO2 and PM10. Pollutants were mainly emitted during cruise and hotelling modes, and the highest intensities of emissions located in the vicinity of fairways, berth and anchorage areas in Tianjin Port. Distinctive difference between the lowest (February) and the highest (September) monthly emissions is due to the adjustment of freight volume during the Chinese New Year and the months before and after it.
•First ship emission inventory with high temporal-spatial resolution in Tianjin Port.•Nearly all identified vessels equipped with AIS were included in the calculation.•Detailed spatial distribution and monthly variation of ship emissions were presented.
Automatic identification system (AIS) is a maritime communication system that uses a transceiver to automatically exchange navigational data. These data help navigation and allow to monitor the ...maritime traffic. However, this system can be hacked and malicious users can easily transmit false data to mislead the coastguards or vessels navigating around. While previous research has proposed methods to detect these falsifications, none of them suggest strategies that detect AIS identity spoofing combining the tracking of the ship position and AIS transceiver’s carrier frequency offset (CFO). The CFO, caused by the carrier frequencies mismatch between emitter and receiver and Doppler effect, is used as a radiometric signature to identify materially every transceiver independently of its transmitted identity. It can drift over time and this is why it is tracked thanks to a Kalman filter (KF). In addition, position is also considered to reduce the miss probability of spoofing detection. The KF is noise adaptive to be robust against various CFO drifts and noise levels of the environment. The strategy is tested on real AIS data and the results demonstrate its efficacy: false alarm and miss probabilities were respectively 1% and 1.7%. These results show the ability of the test to correctly detect identity spoofing and the interest of CFO as a radiometric signature. This signature, used for the first time in an AIS application, could be used with other signatures in a future work to improve identity spoofing detection. This is why we made open source in GitHub our algorithm and the real AIS data used.
•Based on the encounter evolution, a method is proposed to enable the construction of a ship collision risk model.•A PVO model is developed with an elliptic conflict region, utilizing ddv and tdv to ...quantify state risk of velocities.•A collision risk evolution model is introduced, incorporating spatial attributes, macro-level and micro-level decisions.•Historical scenarios and encounter stages are employed to determine model parameters and their weights.
Aiming at realizing collision risk quantitative evaluation among encounter ships, a novel data-driven evolution model is proposed concerning encounter evolution in maritime transportation. A probabilistic velocity obstacle with an elliptic conflict region is constructed by taking into account uncertainty. The degree of and time to domain violation are introduced to quantify collision risk levels under varying velocities. Then, a ship collision risk evolution model is formulated by considering spatial attributes, macro-level and micro-level evolution based on a realistic collision avoidance decision. The model parameters and their weights are determined by statistical analysis of historical encounter scenarios and the characteristics of encounter stages. Therefore, the model encapsulates the statistical characteristics of actual data, which improves its practical values. The results of case studies indicate that the collision risk evolution model can properly reflect collision risk, so that collision evolution stages can be classified accordingly for rational anti-collision guidance. It brings new contributions to risk visualization, collision avoidance decision-making, and collision accident analysis and responsibility determination.
Water covers 71% of the Earth's surface, where the steady increase in oceanic activities has promoted the need for reliable maritime communication technologies. The existing maritime communication ...systems involve terrestrial, aerial, and space networks. This paper presents a holistic overview of the different forms of maritime communications and provides the latest advances in various marine technologies. The paper first introduces the different techniques used for maritime communications over the radio frequency (RF) and optical bands. Then, we present the channel models for RF and optical bands, modulation and coding schemes, coverage and capacity, and radio resource management in maritime communications. After that, the paper presents some emerging use cases of maritime networks, such as the Internet of Ships and the ship-to-underwater Internet of things. Finally, we highlight a few exciting open challenges and identify a set of future research directions for maritime communication, including bringing broadband connectivity to the deep sea, using terahertz and visible light signals for on-board applications, and data-driven modeling for radio and optical marine propagation.
Data-driven methods open up unprecedented possibilities for maritime surveillance using automatic identification system (AIS) data. In this work, we explore deep learning strategies using historical ...AIS observations to address the problem of predicting future vessel trajectories with a prediction horizon of several hours. We propose novel sequence-to-sequence vessel trajectory prediction models based on encoder–decoder recurrent neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory samples given previous observations. The proposed architecture combines long short-term memory RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data. Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority (DMA) show the effectiveness of deep learning methods for trajectory prediction based on sequence-to-sequence neural networks, which achieve better performance than baseline approaches based on linear regression or on the multilayer perceptron architecture. The comparative evaluation of results shows: first, the superiority of attention pooling over static pooling for the specific application, and second, the remarkable performance improvement that can be obtained with labeled trajectories, i.e., when predictions are conditioned on a low-level context representation encoded from the sequence of past observations, as well as on additional inputs (e.g., port of departure or arrival) about the vessel's high-level intention, which may be available from AIS.
COLREGs-based collision risk awareness model is urgently needed in real-time operating conditions. However, this is a complicated problem under various encounter situations, some of which are very ...complex. In order to quantify the collision risk in real operating conditions, a novel risk-informed collision risk awareness approach is proposed for real-time operating conditions. Firstly, the ship's actions are identified based on the Automatic Identification System (AIS) data. Secondly, the uncertainty of ship action patterns is analyzed by regarding the target ships as velocity obstacles. Then, the collision risk model is utilized to assess the collision risk level based on the uncertainty in the non-linear velocity obstacles algorithm considering responsibility. Finally, some case studies are carried out based on the proposed model. In the model, the dynamic and uncertainty features of the ship action dynamics in real operating conditions are considered, which could benefit on reducing ship collision accidents and improving the development of technologies on intelligent collision avoidance decision makings.
•A novel risk-informed collision risk awareness approach is proposed for real-time operating conditions.•An improved velocity obstacles method called Uncertainty Non-Linear Velocity Obstacles (UNLVO) is raised.•Ship motions are identified by different compression thresholds based on AIS data in real operating conditions.•The dynamic and uncertainty characteristics of ship motion patterns in real operating conditions are considered.
This paper proposes a novel data-driven approach to estimate the navigable capacity of busy waterways, focusing on ships entering and leaving port, based on the structural characteristics of traffic ...flow driven by the Automatic Identification System (AIS) data. First, we collect the ship traffic flow in a busy waterway by processing the original AIS data and then identify the structural characteristics of the traffic flow using the K-means clustering algorithm. The clusters are constructed based on the spatiotemporal consumption of waterway resources of different ships and the waste of waterway resources caused by navigational mode conversion, taking ship domain into consideration. We apply the proposed approach to estimate the navigable capacity of the Dagusha Channel of Tianjin Port, China. The empirical results reveal that the maximum daily traffic capacity of the Dagusha Channel is about 109 ship times/day. A comparison of waterway capacity estimation methods demonstrates that our proposed approach is more accurate and able to quantify the waterway capacity of different types of ships in a busy waterway, taking the structural characteristics of traffic flow explicitly into account. The proposed approach provides support for the design of channel and determination of scheduling schemes for ships in busy waterways.
•Incorporating structural characteristics of traffic flow and various constraints in navigable capacity estimation.•Identifying structural characteristics of traffic flow using K-means based on real operational conditions.•Quantifying ship safety domains and spatial-temporal resource consumptions for each cluster of ships.•Offering a novel approach to estimate the navigable capacity of busy waterways.
Proactive traffic management is increasingly critical in maritime intelligent transportation systems. Central to this is maritime traffic forecasting, which leverages specific structures and ...properties of the problem. This study focuses on the traffic dynamics within convergent areas of inland waterways and proposes a method based on data mining followed by prediction using Automatic Identification System (AIS) data. This approach addresses uncertainties in ship voyage destinations and optimizes predictions for temporary stops in inland waterways. AIS data is processed to depict complete ship motion trajectories, grouping them into trajectory sets based on shared origin, destination, and route. These groups help represent maritime traffic patterns using the entrance and exit points of channels and the boundaries of the study area. Additionally, a stop detection model is applied to these trajectories to identify nodes within maritime traffic networks. A decision tree algorithm is then employed to train a classifier for predicting traffic patterns. The method was validated in the convergent area of the Yangtze River and the Hanjiang River, demonstrating effective pattern extraction from inland maritime traffic and high accuracy in predicting single ship trajectories, achieving a 96.7% accuracy rate and 80.9% precision. The findings suggest that the proposed method (1) effectively extracts and predicts traffic patterns, (2) identifies congestion in convergent waters, and (3) supports traffic management strategies.
The trajectory of the inland waterway ship is important and useful in analysing the features of the ship behaviour and simulating traffic flows. In the proposed research, a method is designed to ...restore the trajectory of an inland waterway ship based on the Automatic Identification System (AIS) data. Firstly, three rules are developed to identify and remove the inaccurate data, based on the reception range of the received AIS data and the manoeuvring characteristics of the inland waterway ship. Secondly, the method of restoring the full trajectory incorporating navigational features of the inland waterway ship is proposed to model the ship trajectory. The trajectory is characterised by three types (line, curve and arc) and five steps (line, curve, arc, curve and line) during the turning section. In order to validate the proposed method, the AIS data of two inland waterway ships collected from three AIS-base-stations is selected for the analysis, all inaccurate AIS data is identified and removed by the use of three cleansing rules. The results show that the three developed rules can effectively identify the inaccurate AIS data. The AIS data collected by an AIS-shipboard-unit is then used to: (1) restore the ship trajectory, and (2) validate the proposed method by comparing the reconstituted trajectories with the actual trajectory. This actual trajectory is determined from intermediate higher frequency Global Positioning System (GPS) data and collected from the AIS-shipboard-unit. The residual errors are calculated as the differences between the estimated latitude values of the restored trajectory functions and the real latitude values of the GPS data. Three alternative methods of trajectory restoring are also evaluated. The results show that the proposed method can be used to restore the full trajectory in an effective manner by using AIS data.
•Three rules are built to identify the inaccurate data and cleanse AIS data.•These three rules can effectively identify and remove the inaccurate data.•The trajectory of the inland ship can be characterised by three trajectory types.•A novel method of restoring the full trajectory is designed.•The proposed method can be used to restore a trajectory in an effective manner.
•Create a database from more than 141 million AIS records.•Propose a random forest-based model to measure the similarity between trajectories.•Develop a decision strategy for vessel destination ...prediction.
Shipping is one of the major transportation approaches around the world. With the growing demands for global shipping service, vessel destination prediction has shown its significant role in improving the efficiency of decision making in industry and ensuring a safe and efficient maritime traffic environment. Currently, most vessel destination prediction methods focus on regional destination prediction, which has restrictions on destinations and regions. Thus, this paper proposes a general AIS (Automatic Identification System) data-driven model for vessel destination prediction. In this random forest-based model, the similarity between the vessel’s traveling and historical trajectories are measured and utilized to predict the destination. The destination of the historical trajectory, which shares the highest similarity with the traveling trajectory, is predicted as the vessel’s destination. The method is different from previous work which used maritime records as input to predict the destination. In our method, a historical trajectory database was generated from more than 141 million AIS records, which covers 534,824 traveling patterns between ports and more than 5.9 million historical trajectories. Comparative studies were carried out to validate the performance of the proposed model, where eleven state-of-the-art trajectories similarity measurement methods combined with two different decision strategies were implemented and compared. The experimental results demonstrate that the proposed model combined with the port frequency-based decision strategy achieves the best prediction accuracy on 35,937 testing trajectories.
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