Paper Info
Title
Extracting Global Shipping Networks from Massive Historical Automatic Identification System Sensor Data: A Bottom-Up Approach.
Abstract
The increasing availability of big Automatic Identification Systems (AIS) sensor data offers great opportunities to track ship activities and mine spatial-temporal patterns of ship traffic worldwide. This research proposes a data integration approach to construct Global Shipping Networks (GSN) from massive historical ship AIS trajectories in a completely bottom-up way. First, a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is applied to temporally identify relevant stop locations, such as marine terminals and their associated events. Second, the semantic meanings of these locations are obtained by mapping them to real ports as identified by the World Port Index (WPI). Stop events are leveraged to develop travel sequences of any ship between stop locations at multiple scales. Last, a GSN is constructed by considering stop locations as nodes and journeys between nodes as links. This approach generates different levels of shipping networks from the terminal, port, and country levels. It is illustrated by a case study that extracts country, port, and terminal level Global Container Shipping Networks (GCSN) from AIS trajectories of more than 4000 container ships in 2015. The main features of these GCSNs and the limitations of this work are finally discussed.
Year
DOI
Venue
2019
10.3390/s19153363
SENSORS
Keywords
Field
DocType
AIS big data,ship trajectory,shipping network,DBSCAN,stay locations,stop events
Data integration,Data mining,Port (computer networking),World Port Index,Top-down and bottom-up design,Electronic engineering,Automatic Identification System,Engineering,Cluster analysis,DBSCAN
Journal
Volume
Issue
Citations 
19
15
1
PageRank 
References 
Authors
0.40
0
3
Name
Order
Citations
PageRank
Zhihuan Wang111.07
Christophe Claramunt210.73
Yinhai Wang329239.37