A novel high-dimensional trajectories construction network based on multi-clustering algorithm

Ren, Feiyang; Han, Yi; Wang, Shaohan; Jiang, He

doi:10.1186/s13638-022-02108-4

Cited by 9 publications

(4 citation statements)

References 30 publications

(31 reference statements)

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“…Zhang et al utilized data-driven algorithms, including density-based spatial clustering of applications with noise (DBSCAN) and Ant Colony Optimization (ACO), to infer vessel routes from AIS data [10]. Ren et al introduced a network based on a multi-clustering algorithm combining k-means, DBSCAN, and Affinity Propagation (AP) clustering methods to generate high-dimensional trajectories and measure their similarity [11]. Eljabu et al emphasized the significance of automatic methods for extracting traffic routes from AIS data, demonstrating the potential of density-based clustering algorithms [12].…”

Section: Historical Analysismentioning

confidence: 99%

Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data

Sedaghat,

Arbabkhah,

Jafari Kang

et al. 2024

JMSE

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This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, compressing, and enhancing it with additional attributes such as online traffic volume, origin/destination, vessel trips, trip direction, and vessel routing. This processed data, enriched with valuable details, serves as an alternative to raw AIS data stored in a centralized database. For user interactions, a user interface is designed to query the database and provide real-time information on a map-based interface. To deal with false or missing AIS records, two methods, dead reckoning and machine learning techniques, are employed to anticipate the trajectory of the vessel in the next time steps. To evaluate each method, several metrics are used, including R squared, mean absolute error, mean offset, and mean offset from the centerline. The functionality of the proposed system is showcased through a case study conducted in the Gulf Intracoastal Waterway (GIWW). Three years of AIS data are collected and processed as a simulated API to transmit AIS records every five minutes. According to our results, the Seq2Seq model exhibits strong performance (0.99 R squared and an average offset of ~1400 ft). However, the second scenario, dead reckoning, proves comparable to the Seq2Seq model as it involves recalculating vessel headings by comparing each data point with the previous one.

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Section: Historical Analysismentioning

confidence: 99%

Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data

Sedaghat,

Arbabkhah,

Jafari Kang

et al. 2024

JMSE

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“…Therefore, it is only necessary to consider the current distance between the agent and the destination port. To calculate this distance, the paper utilizes the Chebyshev distance, as shown in (12).…”

Section: B Dqn Network Parameter Settingmentioning

confidence: 99%

“…Path planning encompasses traditional path planning algorithms, bio-inspired intelligent algorithms, and deep reinforcement learning (DRL) algorithms. Currently, path planning is applied in various domains, including robotics [5,6], unmanned aerial vehicles [7,8], autonomous vehicles [9,10], road network construction through way-points [11,12], map navigation [13], and ship navigation [14]. Unlike path planning for robots and autonomous vehicles, path planning for large vessels such as ships presents unique challenges due to their size and the complexity of maneuvering in narrow waters.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Path Planning Method for Container Ships in Bohai Bay Based on Improved Deep Q-Learning

Gao,

Dong,

Han

2023

IEEE Access

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In response to the limitations of the DQN algorithm in adaptability, which result in a low success rate in ship path planning, this paper introduces an improved algorithm based on Deep Q-learning (DQN) to enhance path planning. The proposed algorithm aims to plan a reasonable and cost-effective route to the destination based on all historical track, regardless of the current location of ship within the environment. Firstly, the k-means clustering algorithm is employed to cluster the historical ship locations. The value of k is progressively increased to include more locations, allowing the model to interact with the environment of increasing complexity. This approach enhances the generalization capability of the model by enabling it to autonomously devise a route from any starting point to the destination port. On the other hand, for the ship path problem, the DQN algorithm is enhanced through the optimization of the reward function. This improvement targets the challenges of convergence difficulty and low learning efficiency, which greatly improves the rate of convergence for the model. Finally, the effectiveness of the method is verified by comparing it experimentally in terms of the effectiveness of path planning and model convergence trend. The results demonstrate that the improved DQN algorithm achieves a convergence speed improvement of over 25%. Additionally, with the same training time, the success rate of path planning from any position to destination within the environment is enhanced by 44%. It has better effect on the path planning of ships.INDEX TERMS Ship path planning, AIS, deep reinforcement learning, neural network, k-means

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“…In contrast to the WBMs, with the fast development of computing power and the Internet of Things (IoT), the use data analysis along with data mining tech-Journal of Software Engineering and Applications niques such as machine learning (ML), deep learning (DL) and other statistical analysis are becoming possible [23]- [29]. The BBMs entirely rely on data analysis by processing multi-dimensional data and extracting hidden information from complex dataset.…”

Section: Introductionmentioning

confidence: 99%

Ship Fuel and Carbon Emission Estimation Utilizing Artificial Neural Network and Data Fusion Techniques

Wang¹,

Wang²,

Han³

et al. 2023

JSEA

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Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship's energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship's fuel consumption and carbon emissions under varying weather conditions.

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A novel high-dimensional trajectories construction network based on multi-clustering algorithm

Cited by 9 publications

References 30 publications

Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data

Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data

An Optimized Path Planning Method for Container Ships in Bohai Bay Based on Improved Deep Q-Learning

Ship Fuel and Carbon Emission Estimation Utilizing Artificial Neural Network and Data Fusion Techniques

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