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Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters

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  • Zhang, Liye
  • Meng, Qiang
  • Fang Fwa, Tien

Abstract

This study develops a tangible analytical approach to analyze ship traffic demand and the spatial–temporal dynamics of ship traffic in port waters using big AIS data. By applying the developed approach to the Singapore port waters, we find that the origin-to-destination pairs and navigation routes in the Singapore port waters keep stable over time. Furthermore, there are several hotspot areas in the Singapore Strait where ship sailing speeds are relatively high and ship sailing speeds in a few water areas vary greatly. More interestingly, we find that these hotspot areas well coincide with the spatial distribution of ship accidents.

Suggested Citation

  • Zhang, Liye & Meng, Qiang & Fang Fwa, Tien, 2019. "Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 287-304.
  • Handle: RePEc:eee:transe:v:129:y:2019:i:c:p:287-304
    DOI: 10.1016/j.tre.2017.07.011
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    14. Zhang, Weibin & Feng, Xinyu & Goerlandt, Floris & Liu, Qing, 2020. "Towards a Convolutional Neural Network model for classifying regional ship collision risk levels for waterway risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
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    16. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Yang, Zaili, 2023. "Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
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