AIS Trajectories Simplification and Threshold Determination

Zhang, Shu-Kai; Liu, Zhengjiang; Cai, Yao; Wu, Zedong; Shi, Guangming

doi:10.1017/s0373463315000831

Cited by 60 publications

(35 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The value depends on the analysis and the trajectories. Some research, however, has tried to create guidelines (Zhang et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

Simplification and Event Identification for AIS Trajectories: the Equivalent Passage Plan Method

Sánchez-Heres

2018

J. Navigation

View full text Add to dashboard Cite

Two pre-processes for Automatic Identification System (AIS) trajectories commonly reported in the maritime knowledge discovery literature are trajectory simplification and event identification. Both pre-processes reduce storage and computational expenses by reducing the number of data points to be used in an analysis. This paper presents an event identification and trajectory simplification method based on behaviour identification and translation. Trajectory segments deemed to correspond to coastal or ocean navigation are translated into equivalent passage plan segments; a succinct description of the movements and behaviour of the ship. As a trajectory simplification method, it provides two main advantages over commonly used trajectory simplification methods: more meaningful simplified trajectories with better encoding of basic behaviours and the possibility to retain interesting behaviours in full resolution. As an event identification method, it is capable of differentiating between normal ocean or coastal navigating behaviour and complex or interesting behaviour, such as pilotage, reaction to a traffic conflict, or an involuntary deviation from the passage plan.

show abstract

“…The value depends on the analysis and the trajectories. Some research, however, has tried to create guidelines (Zhang et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

Simplification and Event Identification for AIS Trajectories: the Equivalent Passage Plan Method

Sánchez-Heres

2018

J. Navigation

View full text Add to dashboard Cite

show abstract

“…De Vries and Van Someren [18] used DP algorithm to retain the stop and move information on the ship's trajectory better. Zhang [19] et al…”

Section: Introductionmentioning

confidence: 99%

Vessel AIS Trajectory Online Compression Based on Scan-Pick-Move Algorithm Added Sliding Window

et al. 2020

View full text Add to dashboard Cite

The trajectory data of vessel AIS (automatic identification system) has important theoretical and application value for information supporting decisions. However, large sizes lead to difficulties in storing, querying, and processing. To solve the problems of high compression ratio and longtime consumption of the existing online trajectory compression algorithm, an SPM (scan-pick-move) trajectory data compression algorithm added sliding window is proposed. In order to better compress vessel trajectory data regarding compression efficiency, the sliding window is added to the classical SPM algorithm. In order to reduce trajectory data storage space, the maximum offset distance reference trajectory point is used as the criterion of whether the current trajectory point can be compressed. In this paper, the multi-dimensional space-time characteristics of trajectory data, such as distance error, compression ratio and compression time, are selected to evaluate the trajectory compression method from three levels: geometric characteristics, motion characteristics and compression efficiency. Compared with the existing SPM trajectory data compression algorithm, parallel experiments are conducted based on AIS data gathered over the duration of a month in the Japan Osaka Bay. The SPM trajectory compression algorithm added sliding window can significantly reduce the compression time and outperforms other existing trajectory compression algorithms in term of average compression error at high compression strengths. Also, the proposed method has high compression efficiency in the range of commonly used compression thresholds.

show abstract

“…Sheng et al [26] extracted 17 types of trajectory features based on the basic movement patterns and the sub-trajectory segments and divided the ship trajectory into three basic movement patterns (anchored-off pattern, turning pattern, and straight-sailing pattern). Many researchers (Zhu et al [10], Li et al [27], Zhang et al [9,28,29,30], Zhao et al [31,32], Mou et al [33], Ren et al [34]) have used the Douglas–Peucker (DP) algorithm for the pre-processing of AIS big data. However, this algorithm has high time complexity and it is time-consuming to run.…”

Section: Introductionmentioning

confidence: 99%

Ship Spatiotemporal Key Feature Point Online Extraction Based on AIS Multi-Sensor Data Using an Improved Sliding Window Algorithm

Gao

Shi

2019

Sensors

Self Cite

View full text Add to dashboard Cite

Large volumes of automatic identification system (AIS) data provide new ideas and methods for ship data mining and navigation behavior pattern analysis. However, large volumes of big data have low unit values, resulting in the need for large-scale computing, storage, and display. Learning efficiency is low and learning direction is blind and untargeted. Therefore, key feature point (KFP) extraction from the ship trajectory plays an important role in fields such as ship navigation behavior analysis and big data mining. In this paper, we propose a ship spatiotemporal KFP online extraction algorithm that is applied to AIS trajectory data. The sliding window algorithm is modified for application to ship navigation angle deviation, position deviation, and the spatiotemporal characteristics of AIS data. Next, in order to facilitate the subsequent use of the algorithm, a recommended threshold range for the corresponding two parameters is discussed. Finally, the performance of the proposed method is compared with that of the Douglas–Peucker (DP) algorithm to assess its feature extraction accuracy and operational efficiency. The results show that the proposed improved sliding window algorithm can be applied to rapidly and easily extract the KFPs from AIS trajectory data. This ability provides significant benefits for ship traffic flow and navigational behavior learning.

show abstract

AIS Trajectories Simplification and Threshold Determination

Cited by 60 publications

References 27 publications

Simplification and Event Identification for AIS Trajectories: the Equivalent Passage Plan Method

Simplification and Event Identification for AIS Trajectories: the Equivalent Passage Plan Method

Vessel AIS Trajectory Online Compression Based on Scan-Pick-Move Algorithm Added Sliding Window

Ship Spatiotemporal Key Feature Point Online Extraction Based on AIS Multi-Sensor Data Using an Improved Sliding Window Algorithm

Contact Info

Product

Resources

About