Grand challenge: Vessel destination and arrival time prediction with sequence-to-sequence models over spatial grid

Duc Duy Nguyen, Chan Le Van, Muhammad Intizar Ali

Research output: Chapter in Book or Conference Publication/ProceedingConference Publicationpeer-review

13 Citations (Scopus)

Abstract

We propose a sequence-to-sequence based method to predict vessels' destination port and estimated arrival time. We consider this problem as an extension of trajectory prediction problem, that takes a sequence of historical locations as input and returns a sequence of future locations, which is used to determine arrival port and estimated arrival time. Our solution first represents the trajectories on a spatial grid covering Mediterranean Sea. Then, we train a sequence-to-sequence model to predict the future movement of vessels based on movement tendency and current location. We built our solution using distributed architecture model and applied load balancing techniques to achieve both high performance and scalability.

Original languageEnglish
Title of host publicationDEBS 2018 - Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems
PublisherAssociation for Computing Machinery, Inc
Pages217-220
Number of pages4
ISBN (Electronic)9781450357821
DOIs
Publication statusPublished - 25 Jun 2018
Externally publishedYes
Event12th ACM International Conference on Distributed and Event-Based Systems, DEBS 2018 - Hamilton, New Zealand
Duration: 25 Jun 201826 Jun 2018

Publication series

NameDEBS 2018 - Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems

Conference

Conference12th ACM International Conference on Distributed and Event-Based Systems, DEBS 2018
Country/TerritoryNew Zealand
CityHamilton
Period25/06/1826/06/18

Keywords

  • Recurrent neural network
  • Sequence to sequence models

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