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• W2944353629 abstract "The value of ship Automatic Identification System (AIS) data coexists with data noises, whereas a tangible noise elimination method plays an important role in data mining. In this study, we first address the big ship AIS data de-noising issue for the 43 million records from 15 legs in the Singapore Strait, in which ship position, speed, and course data are checked and corrected by our proposed Method-I. Moreover, a winding number-based Method-II is developed to select data within irregularly restricted water areas. Based on these data, we particularly design a search-and-cut Method-III to filter out ships that pass through an “L” turning (two or more neighboring legs forming a curved channel) in this study. Speeds are normally distributed within a certain range according to the real AIS data in the Singapore Strait. Three types of waters (i.e., legs, “L” turnings and the whole strait) are compared. The results indicate that ship speeds in “L” turnings are more normally distributed than the speeds in legs and the entire strait. In the Singapore Strait, ship speeds slow down by 5.26%–14.4% to pass through “L” turnings approximately. Moreover, our tangible Method-IV of identifying ship decelerating processes from a large number of navigational data indicates that changes in ship speed of an “L” turning have the least relationship with ship types or lengths. However, we find that the longer the length of the ship, (1) the longer the decelerating time, (2) the smaller the deceleration, and (3) the smaller the course changing rate." @default.
• W2944353629 created "2019-05-16" @default.
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• W2944353629 date "2019-06-01" @default.
• W2944353629 modified "2023-10-01" @default.
• W2944353629 title "How do ships pass through L-shaped turnings in the Singapore strait?" @default.
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• W2944353629 doi "https://doi.org/10.1016/j.oceaneng.2019.04.033" @default.
• W2944353629 hasPublicationYear "2019" @default.
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