Extracting ship stopping information from AIS data

Zhuang, Y; Cheng, Liang; He, Rong; Hui, Yang

doi:10.1016/j.oceaneng.2022.111004

Cited by 28 publications

(8 citation statements)

References 29 publications

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“…The existing method of stopping segment classification, proposed in the work of Yan et al, demonstrated an accuracy of 93% on the experiment of 202 stopping segments [8]. Here, the proposed approach in this paper is proven to make a significant improvement.…”

Section: Stopping Trajectory Classification To Identify Area and Even...mentioning

confidence: 62%

“…A comprehensive study on extracting shipstopping information from AIS tracks is proposed by Yan et al [8]. The study further divides the ship-stopping events into two stopping modes: berth and anchorage stops.…”

Section: Related Workmentioning

confidence: 99%

“…While the existing study identifies stopping segments and determines the location point of the POI [8], this study makes a further improved approach by identifying the de facto boundary of the port and the zoning of the area: berthing location and anchorage area.…”

Section: Stopping Trajectory Classification To Identify Area and Even...mentioning

confidence: 99%

“…Yan et al proposed a method to extract stopping information from AIS tracks to comprehend vessel behavior within port areas and to provide knowledge for ship traffic analysis [8]. The method demonstrates 94.00 % accuracy in identifying stopping tracks based on their experiment AIS dataset.…”

Section: Introductionmentioning

confidence: 99%

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Port Performance Indicators Construction based on the AIS-generated Trajectory Segmentation and Classification

Wijaya,

Nakamura

2024

Preprint

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With coverage of worldwide shipping routes and straightforward accessibility, vessels' navigational data from AIS have emerged as a potential leading source of knowledge for maritime stakeholders. However, transforming the raw AIS messages into meaningful indicators requires comprehensive work that involves enrichment with multiple relevant data sources. In this study, a fain-grained technique to process AIS tracks for the purpose of constructing port performance indicators is proposed. The technique involves trajectory segmentation to separate the stopping and underway segments of AIS-generated trajectories and trajectory classification to identify moored and at-anchor vessels from the stopping segments. With the fusion of relevant information, the method estimates the port boundary and identifies the associated berthing and anchorage locations to estimate turnaround time and construct port connectivity indicators. AIS datasets recorded over eight months covering the waters of Indonesia, Malaysia, and Singapore, known as one of the world's busiest shipping routes, are selected to evaluate the proposed method. The trajectory segmentation demonstrated 98 - 99% accuracy, while the classification achieved 95 - 97%. The estimation of the vessel turnaround time closely matched the UNCTAD data with an error rate of 2.9%. These results prove the proposed approach's practicality in contributing to the real-world scenario.

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Section: Stopping Trajectory Classification To Identify Area and Even...mentioning

confidence: 62%

Section: Related Workmentioning

confidence: 99%

Section: Stopping Trajectory Classification To Identify Area and Even...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Port Performance Indicators Construction based on the AIS-generated Trajectory Segmentation and Classification

Wijaya,

Nakamura

2024

Preprint

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“…They further applied spatiotemporal analysis to quantify exhaust ship emissions. In addition to the abovementioned applications, the AIS data are also widely used in fields like ocean current detection [17], [18], [19], waterway traffic [20], [21], [22], environmental noise [23], marine fisheries [24], [25], [26], and marine trade [27], [28].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Port performance indicators construction based on the AIS-generated trajectory segmentation and classification

Wijaya,

Nakamura

2024

Int J Data Sci Anal

View full text Add to dashboard Cite

With coverage of worldwide shipping routes and straightforward accessibility, vessels’ navigational data from AIS have emerged as a potential leading source of knowledge for maritime stakeholders. However, transforming the raw AIS messages into meaningful indicators requires comprehensive work that involves enrichment with multiple relevant data sources. In this study, a fain-grained technique to process AIS tracks for the purpose of constructing port performance indicators is proposed. The technique involves trajectory segmentation to separate the stopping and underway segments of AIS-generated trajectories and trajectory classification to identify moored and at-anchor vessels from the stopping segments. With the fusion of relevant information, the method estimates the port boundary and identifies the associated berthing and anchorage locations to estimate turnaround time and construct port connectivity indicators. AIS datasets recorded over eight months covering the waters of Indonesia, Malaysia, and Singapore, known as one of the world’s busiest shipping routes, are selected to evaluate the proposed method. The trajectory segmentation demonstrated 98–99% accuracy, while the classification achieved 95–97%. The estimation of the vessel turnaround time closely matched the UNCTAD data with an error rate of 2.9%. These results prove the proposed approach’s practicality in contributing to the real-world scenario.

show abstract

Extracting ship stopping information from AIS data

Cited by 28 publications

References 29 publications

Port Performance Indicators Construction based on the AIS-generated Trajectory Segmentation and Classification

Port Performance Indicators Construction based on the AIS-generated Trajectory Segmentation and Classification

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

Port performance indicators construction based on the AIS-generated trajectory segmentation and classification

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