Multi-criteria route planning with risk contour map for smart navigation

Jeong, Mingi; Lee, Eun-Bang; Lee, Moonjin; Jung, Jung-Yeul

doi:10.1016/j.oceaneng.2018.11.050

Cited by 38 publications

(25 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. Rong et al [30] adopted the kernel density estimation-based method for mining ships' behaviors from the AIS data.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…MG Jeong et al 7 propose a multi-criteria route planning technique for operators to objectively determine the routes according to their intentions. The technique can be used for the smart navigation and the future autonomous navigation.…”

Section: Related Workmentioning

confidence: 99%

Research on shore-based intelligent vessel support system based on multi-source navigation sensors simulation

Yang

Lian

Ren

2019

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Under the guidance of industry 4.0, peoples pay more attention to the intelligent equipment and system. Intelligent vessel without crew on board has acquired growing attention worldwide over the years. The navigation condition of intelligent vessel without crew at sea should be monitored and controlled by the shore-based intelligent vessel support system. The navigation-related data collected by the multi-source navigation sensors should be presented on the shore-based support system in an efficient and friendly way. This article presents a simulation model for multi-source navigation sensors such as echo sounder and electronic position-fixing system. Furthermore, this work proposes an efficient architecture for the shore-based support system by way of multi-source navigation sensors simulation. The results show that the simulation model of multi-source navigation sensors is efficient for the simulation of echo sounder and electronic position-fixing system. The shore-based intelligent vessel support system based on multi-source navigation sensors simulation can present navigation-related data and monitor the condition of intelligent vessel at sea.

show abstract

“…One of these directions focuses on the concept of smart navigation, such as decision support systems based on automatic radar plotting aids as presented by Ożoga and Montewka (2018), AIS-based maritime spatial planning (Le Tixerant et al, 2018) and route planning (Jeong et al, 2019). Furthermore, new methodology has recently been introduced for autonomous navigation (Naeem et al, 2016) and There exists a body of literature on NCS detection, which in particular relates to real-time applications with the goal of providing increased warning time for sailors for evasion manoeuvres at sea.…”

Section: Introductionmentioning

confidence: 99%

AIS-based near-collision database generation and analysis of real collision avoidance manoeuvres

et al. 2021

View full text Add to dashboard Cite

Economic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vague and do not precisely dictate which, when and how collision avoidance manoeuvres (CAMs) should be executed. The automatic identification system (AIS) is widely used for vessel monitoring and traffic control. This paper presents an efficient, scalable method for processing large-scale raw AIS data using the closest point of approach (CPA) framework. NCSs are identified to create a database of historical traffic data. Important features describing CAMs are defined, estimated and analysed. Applications on a high-quality real-world data set show promising results for a subset of the identified situations. Future applications may play a significant role in the maritime regulatory framework, navigation protocol compliance evaluation, risk assessment, automatic collision avoidance, and algorithm design and testing for autonomous vessels.

show abstract

Multi-criteria route planning with risk contour map for smart navigation

Cited by 38 publications

References 25 publications

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

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

Research on shore-based intelligent vessel support system based on multi-source navigation sensors simulation

AIS-based near-collision database generation and analysis of real collision avoidance manoeuvres

Contact Info

Product

Resources

About