<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.</p></abstract>

The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.

Vessel Crowd Movement Pattern Mining for Maritime Traffic Management

Planning and Operations Group, Singapore Institute of Manufacturing Technology

LOGI – Scientific Journal on Transport and Logistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Vessel Crowd Movement Pattern Mining for Maritime Traffic Management"}

The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and...

Vessel Crowd Movement Pattern Mining for Maritime Traffic Management

Publié en ligne: 26 nov. 2019

Pages: 105 - 115

DOI: https://doi.org/10.2478/logi-2019-0020

Mots clés
Spatio-temporal data mining, travel behavior mining, vector clustering, nonparametric regression, maritime traffic planning and management

© 2019 Rong Wen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Vessel Crowd Movement Pattern Mining for Maritime Traffic Management

Publié en ligne: 26 nov. 2019

Pages: 105 - 115

DOI: https://doi.org/10.2478/logi-2019-0020

Mots clésSpatio-temporal data mining, travel behavior mining, vector clustering, nonparametric regression, maritime traffic planning and management

© 2019 Rong Wen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Spatio-temporal data mining, travel behavior mining, vector clustering, nonparametric regression, maritime traffic planning and management