Spatio-Temporal Vessel Trajectory Clustering Based on Data Mapping and Density

Li, Huanhuan; Liu, Jingxian; Wu, Kefeng; Yang, Zaili; Liu, Ryan Wen; Xiong, Naixue

doi:10.1109/access.2018.2866364

Cited by 134 publications

(75 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By applying the DBSCAN algorithm, lines are mainly divided into three types: density-connected lines, outliers, and core lines. A line is generally classified as a core line if a minimum number of MinPts lines are included within a distance Eps, and the lines that are density connected with others are regarded as the same cluster, while points which are not density connected are regarded as outliers [ 23 ].…”

Section: Modeling Approachmentioning

confidence: 99%

A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network

Suo

Chen

Claramunt

et al. 2020

Sensors

113

View full text Add to dashboard Cite

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.

show abstract

Section: Modeling Approachmentioning

confidence: 99%

A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network

Suo

Chen

Claramunt

et al. 2020

Sensors

113

View full text Add to dashboard Cite

show abstract

“…Theoretical derivation proves that a hidden layer BP-ANN (Back Propagation-Artificial Neural Network) can approximate any nonlinear continuous function with arbitrary precision, so we adopt a three-layer BP-ANN model [Li, Liu, Wu et al (2018); Gao and Liao (2010)]. 1.…”

Section: Ship Trajectory Prediction Model Based On Bp Neural Networkmentioning

confidence: 99%

Ship Trajectory Prediction Based on BP Neural Network

Zhou¹,

Chen²,

Zhang³

2019

Journal on Artificial Intelligence

View full text Add to dashboard Cite

In recent years, with the prosperity of world trade, the water transport industry has developed rapidly, the number of ships has surged, and ship safety accidents in busy waters and complex waterways have become more frequent. Predicting the movement of the ship and analyzing the trajectory of the ship are of great significance for improving the safety level of the ship. Aiming at the multi-dimensional characteristics of ship navigation behavior and the accuracy and real-time requirements of ship traffic service system for ship trajectory prediction, a ship navigation trajectory prediction method combining ship automatic identification system information and Back Propagation (BP) neural network are proposed. According to the basic principle of BP neural network structure, the BP neural network is trained by taking the characteristic values of ship navigation behavior at three consecutive moments as input and the characteristic values of ship navigation behavior at the fourth moment as output to predict the future ship navigation trajectory. Based on the Automatic Identification System (AIS) information of the waters near the Nanpu Bridge in Pudong New Area, Shanghai, the results show that the method is used to predict the ship's navigational behavior eigenvalues accurately and in real time. Compared with the traditional kinematics prediction trajectory method, the model can effectively predict ship navigation. The trajectory improves the accuracy of the ship's motion situation prediction, and has the advantages of high computational efficiency and strong versatility, and the error is within an acceptable range.

show abstract

“…Unfortunately, we did not evaluate the performance nor have we considered spatial data. Decidedly, the literature includes several works related to trajectory clustering [21] and its applications on resolving real world problems [22]. Still, to the best of our knowledge, this is the first work discussing the exploit of the transitive closure on a fuzzy similarity relation to extract clusters of raw trajectories by using Spark.…”

Section: Literaturementioning

confidence: 99%

A Distributed Execution Pipeline for Clustering Trajectories Based on a Fuzzy Similarity Relation

Maguerra¹,

Boulmakoul²,

Karim

et al. 2019

Algorithms

View full text Add to dashboard Cite

The proliferation of indoor and outdoor tracking devices has led to a vast amount of spatial data. Each object can be described by several trajectories that, once analysed, can yield to significant knowledge. In particular, pattern analysis by clustering generic trajectories can give insight into objects sharing the same patterns. Still, sequential clustering approaches fail to handle large volumes of data. Hence, the necessity of distributed systems to be able to infer knowledge in a trivial time interval. In this paper, we detail an efficient, scalable and distributed execution pipeline for clustering raw trajectories. The clustering is achieved via a fuzzy similarity relation obtained by the transitive closure of a proximity relation. Moreover, the pipeline is integrated in Spark, implemented in Scala and leverages the Core and Graphx libraries making use of Resilient Distributed Datasets (RDD) and graph processing. Furthermore, a new simple, but very efficient, partitioning logic has been deployed in Spark and integrated into the execution process. The objective behind this logic is to equally distribute the load among all executors by considering the complexity of the data. In particular, resolving the load balancing issue has reduced the conventional execution time in an important manner. Evaluation and performance of the whole distributed process has been analysed by handling the Geolife project’s GPS trajectory dataset.

show abstract

Spatio-Temporal Vessel Trajectory Clustering Based on Data Mapping and Density

Cited by 134 publications

References 44 publications

A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network

A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network

Ship Trajectory Prediction Based on BP Neural Network

A Distributed Execution Pipeline for Clustering Trajectories Based on a Fuzzy Similarity Relation

Contact Info

Product

Resources

About