Skip to Main Content (Press Enter)

Logo CNR
  • ×
  • Home
  • People
  • Outputs
  • Organizations
  • Expertise & Skills

UNI-FIND
Logo CNR

|

UNI-FIND

cnr.it
  • ×
  • Home
  • People
  • Outputs
  • Organizations
  • Expertise & Skills
  1. Outputs

Uncovering vessel movement patterns from AIS data with graph evolution analysis

Conference Paper
Publication Date:
2020
abstract:
The availability of the large amount of Automatic Identification System (AIS) data has fostered many studies on maritime vessel traffic during the recent years, often representing vessels and ports relationships as graphs. Although the continuous research effort, only a few works explicitly study the evolution of such graphs and often consider coarse-grained time intervals. In this context, our ultimate goal is to fill this gap by providing a systematic study in the graph evolution by considering voyages over time. three years of AIS data from the coastal waters of United States. By mining the arrivals and departures of vessels from ports, we build a graph consisting of vessel voyages between ports.We then provide a study on topological features calculated from such graphs with a strong focus on their temporal evolution. Finally, we discuss the main limitations of our approach and the future perspectives that will spawn from this work.
Iris type:
04.01 Contributo in Atti di convegno
Keywords:
Graph analysis; Time series; Maritime traffic
List of contributors:
MONTEIRO DE LIRA, VINICIUS CEZAR; Carlini, Emanuele
Authors of the University:
CARLINI EMANUELE
Handle:
https://iris.cnr.it/handle/20.500.14243/424903
Full Text:
https://iris.cnr.it//retrieve/handle/20.500.14243/424903/73430/prod_447645-doc_161319.pdf
Book title:
Proceedings of the Workshops of the EDBT/ICDT 2020 Joint Conference
Published in:
CEUR WORKSHOP PROCEEDINGS
Series
  • Overview

Overview

URL

http://ceur-ws.org/Vol-2578/BMDA5.pdf
  • Use of cookies

Powered by VIVO | Designed by Cineca | 26.5.1.0 | Sorgente dati: PREPROD (Ribaltamento disabilitato)