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MPA-IBM Project SAFER: Sense-Making Analytics for Maritime Event Recognition

Author

Listed:
  • Gavin Yeo

    (Singapore Maritime and Port Authority (MPA), Singapore 119963)

  • Shiau Hong Lim

    (IBM Research, Singapore 018983)

  • Laura Wynter

    (IBM Research, Singapore 018983)

  • Hifaz Hassan

    (IBM Research, Singapore 018983)

Abstract

Project SAFER, a collaboration between the Singapore Maritime and Port Authority and the IBM Research Singapore Laboratory, was established to conceptualize, develop, and test new analytics-based technologies to enhance port operations and cater to the increasing growth in vessel traffic in Singapore. The SAFER system addresses areas in maritime management that have historically required significant human effort. Through a common set of machine learning–based models, the SAFER system is able to forecast vessel arrival timings and potential traffic hot spots within port waters as well as to detect unusual behavior of vessels, from illegal bunkering (i.e., transfer of marine fuel) to ships flouting Singapore regulations. The SAFER project has been transformative at the Singapore Maritime and Port Authority. It has demonstrated that significant value can be obtained through the use of analytics in a complex and mission-critical field such as maritime port management.

Suggested Citation

  • Gavin Yeo & Shiau Hong Lim & Laura Wynter & Hifaz Hassan, 2019. "MPA-IBM Project SAFER: Sense-Making Analytics for Maritime Event Recognition," Interfaces, INFORMS, vol. 49(4), pages 269-280, July.
    • Handle: RePEc:inm:orinte:v:49:y:2019:i:4:p:269-280
    DOI: 10.1287/inte.2019.0997
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    References listed on IDEAS

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    1. Krause, Cory M. & Zhang, Lei, 2019. "Short-term travel behavior prediction with GPS, land use, and point of interest data," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 349-361.
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    Cited by:

    1. Michael F. Gorman & John-Paul Clarke & René Koster & Michael Hewitt & Debjit Roy & Mei Zhang, 2023. "Emerging practices and research issues for big data analytics in freight transportation," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 28-60, March.

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