FVRD: Fishing Vessels Relationships Discovery System Through Vessel Trajectory

Vessel monitoring system (VMS) is an effective tool for the quantified study of fishing. As the fishing vessels equipped with VMS clients, a large amount of trajectory data has been collected, which brings a new opportunity for fishing research. According to fishery safety production regulations, fishing vessels should perform grouping operations based on actual conditions, but the group information cannot be collected by the VMS. In this paper, we propose the Fishing Vessels Relationships Discovery system (FVRD) by calculating the interaction time among fishing vessels and then using it as a weight to generate a relationship network. The experiment of the proposed FVRD on the vessel dataset of Zhejiang Province reveals that the generated fishing community is consistent with the type of operation of the fishing vessels, which means the proposed method is effective. The experiment also indicates that the fishing vessel relationship network has the characteristics of small-world and scale-free that is similar to the human social network, Moreover, FVRD shows that 86.78% of vessels share the collaboration relationships over one week, 10.72% of vessels are in the long-term cooperation, confirming the regulation that most fishing vessels are sailing together for fishing.

[1]  Graham J. Pierce,et al.  Identification of métiers of the Northern Spanish coastal bottom pair trawl fleet by using the partitioning method CLARA , 2010 .

[2]  Robert E. Tarjan,et al.  Depth-First Search and Linear Graph Algorithms , 1972, SIAM J. Comput..

[3]  Clara Ulrich,et al.  Detailed mapping of fishing effort and landings by coupling fishing logbooks with satellite-recorded vessel geo-location , 2010 .

[4]  Chen Wang,et al.  Classifying transportation mode and speed from trajectory data via deep multi-scale learning , 2019, Comput. Networks.

[5]  Brendan J. Godley,et al.  A Step Towards Seascape Scale Conservation: Using Vessel Monitoring Systems (VMS) to Map Fishing Activity , 2007, PloS one.

[6]  Feng Hong,et al.  Recognizing fishing activities via VMS trace analysis based on Mathematical Morphology , 2016, 2016 Techno-Ocean (Techno-Ocean).

[7]  Wentong Cai,et al.  An auto-adaptive dead reckoning algorithm for distributed interactive simulation , 1999, Proceedings Thirteenth Workshop on Parallel and Distributed Simulation. PADS 99. (Cat. No.PR00155).

[8]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[9]  M. Lengaigne,et al.  Patterns in the spatial distribution of Peruvian anchovy (Engraulis ringens) revealed by spatially explicit fishing data , 2008 .

[10]  Simon Jennings,et al.  Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data , 2010 .

[11]  Panos Kalnis,et al.  Parallel trajectory similarity joins in spatial networks , 2018, The VLDB Journal.

[12]  Fang Liu,et al.  Trajectory optimization by using EMD- and ICA-based processing method , 2019 .

[13]  Nicolas Bez,et al.  A pioneer validation of a state-space model of vessel trajectories (VMS) with observers’ data , 2010 .

[14]  Yu Zheng,et al.  Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.

[15]  Matthieu Latapy,et al.  Computing Communities in Large Networks Using Random Walks , 2004, J. Graph Algorithms Appl..

[16]  Michel J. Kaiser,et al.  Implications of using alternative methods of vessel monitoring system (VMS) data analysis to describ , 2012 .

[17]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[18]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[19]  Etienne Rivot,et al.  Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian Hidden Markov Models , 2010 .

[20]  ChangShui-Kai,et al.  Deriving high-resolution spatiotemporal fishing effort of large-scale longline fishery from vessel monitoring system (VMS) data and validated by observer data , 2014 .

[21]  Yuan Feng,et al.  FVRD: Fishing Vessel Relations Discovery Through VMS Trace Analysis , 2018, 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO).

[22]  Hao Wang,et al.  Big data and industrial Internet of Things for the maritime industry in Northwestern Norway , 2015, TENCON 2015 - 2015 IEEE Region 10 Conference.

[23]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  Matthieu Latapy,et al.  Main-memory triangle computations for very large (sparse (power-law)) graphs , 2008, Theor. Comput. Sci..

[25]  Simon Washington,et al.  Shortest path and vehicle trajectory aided map-matching for low frequency GPS data , 2015 .

[26]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[28]  Pasquale Pagano,et al.  Deriving fishing monthly effort and caught species from vessel trajectories , 2013, 2013 MTS/IEEE OCEANS - Bergen.

[29]  Lokukaluge P. Perera,et al.  Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction , 2012, IEEE Transactions on Intelligent Transportation Systems.

[30]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[32]  Feng Hong,et al.  FVID: Fishing Vessel Type Identification Based on VMS Trajectories , 2018 .

[33]  Michel J. Kaiser,et al.  The effectiveness of using CPUE data derived from Vessel Monitoring Systems and fisheries logbooks to estimate scallop biomass , 2013 .

[34]  Catherine M. Dichmont,et al.  Can vessel monitoring system data also be used to study trawling intensity and population depletion? The example of Australia's northern prawn fishery , 2005 .

[35]  Shui-Kai Chang,et al.  Application of a vessel monitoring system to advance sustainable fisheries management--Benefits received in Taiwan , 2011 .

[36]  Michele Scardi,et al.  Modeling landings profiles of fishing vessels: An application of Self-Organizing Maps to VMS and logbook data , 2016 .

[37]  Stefano Cataudella,et al.  When behaviour reveals activity: Assigning fishing effort to métiers based on VMS data using artificial neural networks , 2011 .

[38]  Peng Gao,et al.  A parallel online trajectory compression approach for supporting big data workflow , 2017, Computing.

[39]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[40]  Chao Liu,et al.  L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis † , 2019, Sensors.