STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network

Liu, Ryan Wen; Liang, Maohan; Nie, Jiangtian; Yuan, Yanli; Xiong, Zehui; Yu, Han; Guizani, Nadra

doi:10.1109/tii.2022.3165886

Cited by 81 publications

(25 citation statements)

References 40 publications

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“…The vessel traffic conflict situation modeling, generated using the dynamic AIS data and social force concept, is embedded into the LSTM network. [13] proposed a spatio-temporal multi-graph convolutional network (STMGCN) based vessel trajectory prediction framework using the mobile edge computing (MEC) paradigm. It is mainly composed of three different graphs, which are, respectively, reconstructed according to the social force, the time to the closest point of approach (TCPA), and the size of surrounding vessels.…”

Section: A Lstmmentioning

confidence: 99%

Network Traffic Prediction Based on LSTM and Transfer Learning

Wan

Liu

et al. 2022

IEEE Access

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The increasing amount of traffic in recent years has led to increasingly complex network problems. To be able to improve overall network performance and increase network utilization, it is valuable to take measures to capture future trends in network traffic. In traditional machine learning, to guarantee the accuracy and high reliability of the models obtained through training, there are two basic assumptions: (1) the training samples used for learning and the new test samples satisfy the condition of independent identical distribution; and (2) there must be enough training samples to learn a good model. However, time-series data are not easily accessible in real life, and even after putting in a lot of time and effort to collect them, the data may be unavailable due to confidentiality. In this paper, a neural network model based on LSTM and transfer learning is proposed to address the problem of small sample size in network traffic prediction. Knowledge in the source domain is transferred to the target domain using transfer learning, and a prediction model with good performance is constructed with a small amount of target domain data. The results show that the performance of the transfer learning model improves by more than 40% over the direct training model when using the same samples for predicting 10,000 rows of data, resulting in better performance of the network traffic prediction task.INDEX TERMS LSTM, network traffic prediction, transfer learning.

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Section: A Lstmmentioning

confidence: 99%

Network Traffic Prediction Based on LSTM and Transfer Learning

Wan

Liu

et al. 2022

IEEE Access

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“…However, in recent years, data-driven temporal sequential prediction methods have obtained good effects [23,24], by which the trajectories of reentry glide targets can be predicted with the help of flight data. On the other hand, in recent years, deep learning technology has shown strong advantages in temporal law prediction [25] and target recognition [26], which had been widely used in vessel trajectory prediction [27,28] and vehicle trajectory prediction [29]. Especially, several complicated temporal sequential prediction problems have been solved based on the recurrent neural network (RNN), which has shown the powerful studying ability of data.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Trajectory Prediction Algorithm for Hypersonic Glide Targets Based on Maneuver Mode Identification

Zhou

Shao

et al. 2022

International Journal of Aerospace Engineering

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Trajectory prediction for hypersonic glide targets is a difficult task that needs to be solved. To improve the prediction precision for hypersonic glide targets, based on the analysis of the target’s maneuver characteristic, an intelligent trajectory prediction algorithm based on the maneuver mode identification is proposed in this paper. Firstly, according to the typical maneuver modes of the target, a group of parameter suitable for maneuver mode identification and parameter estimation is proposed. The proposed maneuver parameters can reflect the maneuvering characteristics of the target than the other control parameters. Then, the rationality of parameters is analyzed. Secondly, using the long-short-term memory network (LSTM), the structure of intelligent trajectory prediction based on maneuver mode identification is proposed. The proposed prediction method is designed to improve the prediction accuracy by combining the target dynamic model with the flight data. Finally, the maneuver trajectory data set is established to train and test the method. For the test data set, when the observation time for the target is 200 s and the prediction time is 150 s, with a fast prediction speed, our method’s average error of spatial distance (AESD) is less than 2.9 km, and the maximum error of spatial distance (MESD) is less than 6.9 km. The result is better than other compared mainstream methods. And it is also proved valid with some observational error.

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“…In recent years, the development of economy has made the shipping industry develop rapidly, while the shipping system [1,2] has gradually become intelligent with the help of the computer technology. To further ensure the safety of ship navigation, ship speed estimation has become an important topic in the study of intelligent shipping, and the popularity of ship automatic identification System (AIS) [3] makes it one of the main channels to obtain ship speed information.…”

Section: Introductionmentioning

confidence: 99%

Extracting Vessel Speed Based on Machine Learning and Drone Images during Ship Traffic Flow Prediction

Zhao

Chen

Zhou

et al. 2022

Journal of Advanced Transportation

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In the water transportation, ship speed estimation has become a key subject of intelligent shipping research. Traditionally, Automatic Identification System (AIS) is used to extract the ship speed information. However, transportation environment is gradually becoming complex, especially in the busy water, leading to the loss of some AIS data and resulting in a variety of maritime accidents. To make up for this deficiency, this paper proposes a vessel speed extraction framework, based on Unmanned Aerial Vehicle (UAV) airborne video. Firstly, YOLO v4 is employed to detect the ship targets in UAV image precisely. Secondly, a simple online and real time tracking method with a Deep association metric (Deep SORT) is applied to track ship targets with high quality. Finally, the ship motion pixel is computed based on the bounding box information of the ship trajectories, at the same time, the ship speed is estimated according to the mapping relationship between image space and the real space. Exhaustive experiments are conducted on the various scenarios. Results verify that the proposed framework has an excellent performance with average speed measurement accuracy is above 93% in complex waters. This paper also paves a way to further predict ship traffic flow in water transportation.

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STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network

Cited by 81 publications

References 40 publications

Network Traffic Prediction Based on LSTM and Transfer Learning

Network Traffic Prediction Based on LSTM and Transfer Learning

An Intelligent Trajectory Prediction Algorithm for Hypersonic Glide Targets Based on Maneuver Mode Identification

Extracting Vessel Speed Based on Machine Learning and Drone Images during Ship Traffic Flow Prediction

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