Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology

Tu, Enmei; Zhang, Guanghao; Rachmawati, Lily; Rajabally, Eshan; Huang, Guang-Bin

doi:10.1109/tits.2017.2724551

Cited by 294 publications

(192 citation statements)

References 129 publications

(135 reference statements)

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“…These techniques, respectively, consist in adapting the speed of the ship to reduce its fuel consumption, optimizing the route the ship has to follow in terms of distance, navigation time or expected consumption, or considering aspects like weather forecasts to avoid events, like storms, that may affect navigation and force the ship to catch up later, overspending fuel. Tu et al collect in their survey [30] multiple examples of these techniques leveraging Automatic Identification System (AIS) data. These techniques have a great impact on fuel consumption and navigation, due to the non-linearities on the speed/power relation of engines.…”

Section: Literature Reviewmentioning

confidence: 99%

Enabling a green just-in-time navigation through stakeholder collaboration

Aroca

Maldonado

Clari

et al. 2020

Eur. Transp. Res. Rev.

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Background: The shipping industry has grown spectacularly during the last 50 years transporting nowadays, approximately, the 80-90% of goods worldwide. However, maritime transport remains a highly inefficient industry. Only in the last 10-15 years has the industry started studying how to optimize navigation speeds and digitization is just entering ports. Consequently, institutions like the International Maritime Organization (IMO) are pressing towards the adoption of measures that increase the industry efficiency, like Just-In-Time (JIT) operations.Methods and Results: This paper shows why the Sea Traffic Management (STM) concept, based on stakeholder collaboration, is a JIT enabler. To do so, we analyze 1 year of navigation data of 33 ships, estimating the impact of JIT barriers on shipping and showing the benefits that the adoption of STM, with different maturity levels, could provide to the industry. Our evaluation shows that, only for containerships, STM can help reducing by 15-23% of fuel consumption and GHG emissions.

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Section: Literature Reviewmentioning

confidence: 99%

Enabling a green just-in-time navigation through stakeholder collaboration

Aroca

Maldonado

Clari

et al. 2020

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…A growing body of literature describes methods of exploiting AIS data for safety and optimisation of seafaring, traffic analysis, anomaly detection, route extraction and prediction, collision detection, path planning, weather routing and many more (Tu et al, 2018). As the amount of available AIS data grows to massive scales though, researchers are realising that computational techniques must contend with difficulties faced when acquiring, storing, and processing the data.…”

Section: Methodsmentioning

confidence: 99%

Preliminary Inter-comparison of AIS Data and Optimal Ship Tracks

Mannarini

Carelli²,

Zissis³

et al. 2019

TransNav

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Optimal ship tracks computed via the VISIR model are compared to tracks recorded by the Automatic Identification System (AIS). The evaluation regards 43 tracks in the Southern Atlantic Ocean, sailed during 2016-2017 by different bulk carriers. In this exercise, VISIR is fed by wave analysis fields from the Copernicus Marine Environment Monitoring Service (CMEMS). In order to reproduce vessel speed loss in waves, a new methodology is developed, where kinematic information from AIS is fusioned with wave information from CMEMS. Resulting VISIR tracks are analyzed along with AIS tracks in terms of their topological features and duration. The tracks exhibit quite diverse topological shapes, including orthodromic, loxodromic, and other paths with complex and dynamic diversions. The distribution of AIS to VISIR track durations is analyzed in terms of several parameters, such as the AIS to VISIR track length and their Fréchet distance. Model features of VISIR affecting the results are discussed and future developments suggested by the results are outlined.

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“…In the field of waterway transportation, current research related to the prediction of water traffic focuses on progressive water prediction of depth. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate real-time waterway depths, but these data include no direct channel depth information [11]. The large volume ship AIS data are widely used in navigation.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate waterway depths, although these data include no direct channel depth information. We suggest a deep neural network (DNN) based model, called DDTree, for using the real-time AIS data and the data from Global Mapper to predict waterway depth for ships in an accurate and timely way. The model combines a decision tree and DNN, which is trained and tested on the AIS and Global Mapper data from the Nantong and Fangcheng ports on the southeastern and southwestern coast of China. The actual waterway depth data were used together with the AIS data as the input to DDTree. The latest data on waterway depths from the Chinese maritime agency were used to verify the results. The experiments show that the DDTree model has a prediction accuracy of 91.15%. Therefore, the DDTree model can provide an accurate prediction of waterway depth and compensate for the shortage of waterway depth monitoring means. The proposed hybrid DDTree model could improve marine situational awareness, navigation safety, and shipping efficiency, and contribute to smart navigation.

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Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology

Cited by 294 publications

References 129 publications

Enabling a green just-in-time navigation through stakeholder collaboration

Enabling a green just-in-time navigation through stakeholder collaboration

Preliminary Inter-comparison of AIS Data and Optimal Ship Tracks

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

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