Trajectory Prediction for Ocean Vessels Base on K-order Multivariate Markov Chain

Guo, Shuai; Liu, Chao; Feng, Yuan; Hong, Feng; Huang, Haiguang

doi:10.1007/978-3-319-94268-1_12

Cited by 23 publications

(15 citation statements)

References 10 publications

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“…Based on our previous work [21], we put up with a novel algorithm to predict ocean vessel trajectory by leveraging K-order multivariate Markov Chain, and leveraged a MapReduce model to process tremendous vessel trajectory data in parallel. The MapReduce-based architecture of L-VTP is depicted in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…Lokukaluge et al [15,16] utilize machine learning algorithms for tracking ocean vessels and then present an extended Kalman filter to predict an ocean vessel trajectory. Methods in [15,16,21,25] can only realize short-term trajectory prediction.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, even if long-term vessel trajectory prediction could be achieved, how to deal with the privacy issues is still an inevitable challenge. In our previous work [21], we simply leverage a Markov model to predict vessel trajectory without considering privacy issues, which also has an unsatisfactory performance of long-term prediction.…”

Section: Introductionmentioning

confidence: 99%

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L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis

Liu

Guo

Feng

et al. 2019

Sensors

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With the rapid development of marine IoT (Internet of Things), ocean MDTN (Mobile Delay Tolerant Network) has become a research hot spot. Long-term trajectory prediction is a key issue in MDTN. There are no long-term fine-grained trajectory prediction methods proposed for ocean vessels because a vessel’s mobility pattern lacks map topology support and can be easily influenced by the fish moratorium, sunshine duration, etc. A traditional on-land trajectory prediction algorithm cannot be directly utilized in this field because trajectory characteristics of ocean vessels are far different from that on land. To address the problem above, we propose a novel long-term trajectory prediction algorithm for ocean vessels, called L-VTP, by utilizing multiple sailing related parameters and K-order multivariate Markov Chain. L-VTP utilizes multiple sailing related parameters to build multiple state-transition matrices for trajectory prediction based on quantitative uncertainty analysis of trajectories. Trajectories’ sparsity of ocean vessels results in a critical state missing problem of a high-order state-transition matrix. L-VTP automatically traverses other matrices in a specific sequence in terms of quantitative uncertainty results to overcome this problem. Furthermore, the different mobility models of the same vessel during the day and the night are also exploited to improve the prediction accuracy. Privacy issues have been taken into consideration in this paper. A quantitative model considering Markov order, training metadata and privacy leak degree is proposed to help the participant make the trade-off based on their customized requirements. We have performed extensive experiments on two years of real-world trajectory data that include more than two thousand vessels. The experiment results demonstrate that L-VTP can realize fine-grained long-term trajectory prediction with the consideration of privacy issues. The average error of 4.5-hour fine-grained prediction is less than 500 m. In addition, the proposed method can be extended to 10-hour prediction with an average error of 2.16 km, which is also far less than the communication range of ocean vessel communication devices.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis

Liu

Guo

Feng

et al. 2019

Sensors

Self Cite

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show abstract

“…Many factors such as fish distribution, weather conditions, and ocean current have significant effects on their movement [15][16][17], which makes traditional prediction models on land not infeasible for vessel trajectory prediction. For modeling vessels' mobile regularity, some researchers design models based on high-order Markov chain, Bayes model, and deep learning methods [15,16,18]. By splitting area to nets, some machine learning models can also be applied by transferring regression to classification problem [19].…”

Section: Introductionmentioning

confidence: 99%

TPR‐DTVN: A Routing Algorithm in Delay Tolerant Vessel Network Based on Long‐Term Trajectory Prediction

Liu

Jiang

et al. 2021

Wireless Communications and Mobile Computing

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An efficient and low-cost communication system has great significance in maritime communication, but it faces enormous challenges because of high communication costs, incomplete communication infrastructure, and inefficient routing algorithms. Delay Tolerant Vessel Networks (DTVNs), which can create low-cost communication opportunities among vessels, have recently attracted considerable attention in the academic community. Most existing maritime ad hoc routing algorithms focus on predicting vessels’ future contacts by mining coarse-grained social relations or spatial distribution, which has led to poor performance. In this paper, we analyze 3-year trajectory data of 5123 fishery vessels in the China East Sea. Using entropy theory, we observe that the trajectory of the vessel has strongly spatial-temporal distribution regularity, especially when previous states were given. To predict accurate future trajectories, we develop a long-term accurate trajectory prediction model by improving the Bidirectional Long-Short Term Memory (Bi-LSTM) model. Based on predicted trajectories and the confident degree of each prediction step, we propose a series of routing algorithms called TPR-DTVN to achieve efficient communication performance. Finally, we carry out simulation experiments with extensive real data. Compared with existing algorithms, the simulation results show that TPR-DTVN can achieve a higher delivery ratio with lower cost and transmission delay.

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“…In a related work, a Bayesian probability trajectory prediction model based on a Gaussian process is introduced. A K-order multivariate Markov chain is applied to establish a state transition matrix to train a large amount of data and support short-term prediction of ship positions, but this approach is still sensitive to previous ship positions this resulting in low accuracy [ 3 ]. A single ship neighborhood approach based on historical data has also been suggested.…”

Section: Introductionmentioning

confidence: 99%

A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network

Suo

Chen

Claramunt

et al. 2020

Sensors

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Ship trajectory prediction is a key requisite for maritime navigation early warning and safety, but accuracy and computation efficiency are major issues still to be resolved. The research presented in this paper introduces a deep learning framework and a Gate Recurrent Unit (GRU) model to predict vessel trajectories. First, series of trajectories are extracted from Automatic Identification System (AIS) ship data (i.e., longitude, latitude, speed, and course). Secondly, main trajectories are derived by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Next, a trajectory information correction algorithm is applied based on a symmetric segmented-path distance to eliminate the influence of a large number of redundant data and to optimize incoming trajectories. A recurrent neural network is applied to predict real-time ship trajectories and is successively trained. Ground truth data from AIS raw data in the port of Zhangzhou, China were used to train and verify the validity of the proposed model. Further comparison was made with the Long Short-Term Memory (LSTM) network. The experiments showed that the ship’s trajectory prediction method can improve computational time efficiency even though the prediction accuracy is similar to that of LSTM.

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Trajectory Prediction for Ocean Vessels Base on K-order Multivariate Markov Chain

Cited by 23 publications

References 10 publications

L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis

L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis

TPR‐DTVN: A Routing Algorithm in Delay Tolerant Vessel Network Based on Long‐Term Trajectory Prediction

A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network

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