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    Mining Marine Vessel AIS Data to Inform Coastal Structure Management

    Source: Journal of Waterway, Port, Coastal, and Ocean Engineering:;2020:;Volume ( 146 ):;issue: 002
    Author:
    Brandan M. Scully
    ,
    David L. Young
    ,
    James E. Ross
    DOI: 10.1061/(ASCE)WW.1943-5460.0000550
    Publisher: ASCE
    Abstract: This study demonstrates the use of a multiyear country-scale automatic identification system (AIS) data set to partition a nationwide portfolio of navigation structures managed by the US Army Corps of Engineers (USACE) into affinity groups based on emergent vessel traffic characteristics. The marine vessel AIS was originally intended to prevent the collision of ships at sea. As a remote sensing technology, it provides continuous monitoring for marine vessel traffic and has enabled a variety of unforeseen applications. The methodology presented uses spatial distance criteria to identify vessel traffic local to each structure. Metrics characterizing traffic behavior including traffic composition, spatial position, trip frequency, and traffic seasonality are derived from vessel data. AIS-derived metrics are combined into feature vectors describing each structure. Pearson correlation of feature vectors with r-neighborhood pruning of the affinity matrix is used to identify similar structure pairs. Semisynchronous label propagation is used to partition the structure portfolio graph into prototype groups with strong similarity in underlying traffic characteristics that may be further refined to align maintenance activity with organizational goals.
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      Mining Marine Vessel AIS Data to Inform Coastal Structure Management

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4264751
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    • Journal of Waterway, Port, Coastal, and Ocean Engineering

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    contributor authorBrandan M. Scully
    contributor authorDavid L. Young
    contributor authorJames E. Ross
    date accessioned2022-01-30T19:09:08Z
    date available2022-01-30T19:09:08Z
    date issued2020
    identifier other%28ASCE%29WW.1943-5460.0000550.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4264751
    description abstractThis study demonstrates the use of a multiyear country-scale automatic identification system (AIS) data set to partition a nationwide portfolio of navigation structures managed by the US Army Corps of Engineers (USACE) into affinity groups based on emergent vessel traffic characteristics. The marine vessel AIS was originally intended to prevent the collision of ships at sea. As a remote sensing technology, it provides continuous monitoring for marine vessel traffic and has enabled a variety of unforeseen applications. The methodology presented uses spatial distance criteria to identify vessel traffic local to each structure. Metrics characterizing traffic behavior including traffic composition, spatial position, trip frequency, and traffic seasonality are derived from vessel data. AIS-derived metrics are combined into feature vectors describing each structure. Pearson correlation of feature vectors with r-neighborhood pruning of the affinity matrix is used to identify similar structure pairs. Semisynchronous label propagation is used to partition the structure portfolio graph into prototype groups with strong similarity in underlying traffic characteristics that may be further refined to align maintenance activity with organizational goals.
    publisherASCE
    titleMining Marine Vessel AIS Data to Inform Coastal Structure Management
    typeJournal Paper
    journal volume146
    journal issue2
    journal titleJournal of Waterway, Port, Coastal, and Ocean Engineering
    identifier doi10.1061/(ASCE)WW.1943-5460.0000550
    page04019042
    treeJournal of Waterway, Port, Coastal, and Ocean Engineering:;2020:;Volume ( 146 ):;issue: 002
    contenttypeFulltext
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    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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