Compressing AIS Trajectory Data Based on the Multi-Objective Peak Douglas–Peucker Algorithm

Abstract

The automatic identification system (AIS) provides a massive database for ocean science. The original AIS data are redundant. Direct use will cause a waste of data storage space and computation costs; hence, data compression must be performed. The Douglas-Peucker algorithm (DP) is an effective trajectory compression algorithm that can well preserve the spatial characteristics of a trajectory but has the following shortcomings: first, it has poor track recovery when compressing multi-turn routes; second, it does not consider the ship speed and heading; and third, it may have the wrong result of the compressed trajectory crossing the obstacle. To address these situations, this study proposes a multi-objective peak DP algorithm (MPDP) that adopts a peak sampling strategy, considers three optimization objectives (spatial characteristics, heading and speed) of trajectory and adds an obstacle detection mechanism to realize a compression algorithm more suitable for curved trajectories. The classical DP algorithm is compared with the MPDP algorithm by simulating trajectory and real trajectory experiments. The results show that the MPDP algorithm optimizes the length loss rate, simultaneous Euclidean distance, and average deviations of the speed and the heading while maintaining a high compression rate similar to that of the DP algorithm. Moreover, it can also successfully avoid obstacles. The optimization effect is most obvious for the multi-turn or hovering trajectory. The optimization rate of length loss, synchronous Euclidean distance, and average deviation of the heading can reach 40%.INDEX TERMS AIS data, Douglas-Peucker algorithm (DP), multi-objective peak Douglas-Peucker algorithm (MPDP), trajectory compression.

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

“…Te red point includes the defection position of the trajectory shape, and the overall similarity of the trajectory before and after compression is relatively high. Integration of part into T_set tuple (19) else if (20) Integration of intersect (part, IP) into I_set tuple (21) end if (22) end for (23) are mainly used in the study as the main factors to measure the temporal characteristics. However, the window size and input threshold set in the sliding window algorithm also have an impact on the fnal compression efect [36,37].…”

“…Although the problem of low threshold input and low operating efciency of the DP algorithm has been solved, it can only ensure the similarity of the trajectory shape when performing trajectory compression, ignoring the index of the time dimension, and the reliability of the compressed data is low. Some scholars have attempted to consider additional information beyond vessel position data during the compression process [19][20][21][22]. Shi and Liu [19] proposed a multifactor DP algorithm that considers speed variations in AIS data and sets a turning threshold.…”

“…However, this approach introduces more threshold parameters and increases the time complexity on top of the DP algorithm. Building on this, Zhou et al [20] proposed a multiobjective peak-based DP algorithm. Tey incorporated peak sampling strategies during the compression process, considering spatial characteristics, heading, and speed information of vessel trajectories.…”

Efficient Large-Scale GPS Trajectory Compression on Spark: A Pipeline-Based Approach

“…Some studies considered the road network [20][21][22][23][24], where the GPS trajectory is located, converted the original GPS trajectory into segment sequences, and then compressed the segment sequences. In addition to the compression of vehicle GPS trajectories, some studies explored the compression problem [25][26][27][28][29] for ship GPS trajectories collected by AIS. Beyond GPS trajectory compression in offline scenarios, some studies have also delved into real-time GPS trajectory compression algorithms.…”

Unmanned Vessel Collision Avoidance Algorithm by Dynamic Window Approach Based on COLREGs Considering the Effects of the Wind and Wave

“…Ref. [18] proposes the multi-objective peak DP algorithm (MPDP), which uses a peak sampling strategy that takes into account the three optimization objectives of trajectory and adds an obstacle detection mechanism to achieve a compression algorithm that is more suitable for curved trajectories. Ref.…”