Generating bunkering statistics from AIS data: A machine learning approach

In shipping, the optimization of the bunkering location is dependent on price, deviation from the planned route and the cost of delays incurred by the bunkering operation itself. Despite their potential importance, detailed statistics for bunkering operations at the individual port call level (e.g. waiting times, barge capacity, location - anchorage or terminal) are not available. The author develops a new method, based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, that can a) identify tanker vessels used as bunkering tankers, b) detect stationary ocean-going vessels at anchorage or alongside terminals and c) automatically recognize bunkering operations as a rendezvous between an ocean-going vessel and a bunkering barge. The author finds that the high time complexity of the DBSCAN algorithm in this setting can be compensated by adjusting the algorithm to distributed computer settings. In the empirical study, The author uses the output to describe the relative importance of Mediterranean ports for bunkering and provide statistics on waiting and servicing times. The empirical findings are important for the optimization of the bunkering location decision in shipping and studies on regional port competitiveness.

• Record URL:
  https://doi.org/10.1016/j.tre.2021.102495
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S136655452100257X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13665545
• Supplemental Notes:
  • © 2021 The Author. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Fuentes, Gabriel
• Publication Date: 2021-11

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Serial:
  • Transportation Research Part E: Logistics and Transportation Review
  • Volume: 155
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1366-5545
  • Serial URL: http://www.sciencedirect.com/science/journal/13665545

Subject/Index Terms

• TRT Terms: Bunkering; Machine learning; Port operations; Shipping
• Geographic Terms: Mediterranean Region
• Subject Areas: Marine Transportation; Terminals and Facilities;

Filing Info

• Accession Number: 01787307
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 5 2021 11:54AM