• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.277-279
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• 2022

Rapid Development and adoption of AIS as a survailance tool has resulted in widespread application of data analysis technology, in addition to AIS ship trajectory clustering. AIS data-based clustering has become an increasingly popular method for marine traffic pattern recognition, ship route prediction and anomaly detection in recent year. In this paper we propose a route waypoint extraction by clustering ships CoG variance trajectory using Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm in both port approach channel and coastal waters. The algorithm discovers route waypoint effectively. The result of the study could be used in traffic route extraction, and more-so develop a maritime anomaly detection tool.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.31-70
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• 2021

The purpose of this paper is to measure the clustering change and analyze empirical results, and choose the clustering ports for Busan, Incheon, and Gwangyang ports by using Hierarchical clustering(single, complete, average, and centroid), Silhouette, and 2SCE[the Second Stage(Type II) cross-efficiency] matrix clustering models on Asian container ports over the period 2009-2018. The models have chosen number of cranes, depth, birth length, and total area as inputs and container TEU as output. The main empirical results are as follows. First, ranking order according to the efficiency increasing ratio during the 10 years analysis shows Silhouette(0.4052 up), Hierarchical clustering(0.3097 up), and 2SCE(0.1057 up). Second, according to empirical verification of the Silhouette and 2SCE models, 3 Korean ports should be clustered with ports like Busan Port[ Dubai, Hong Kong, and Tanjung Priok], and Incheon Port and Gwangyang Port are required to cluster with most ports. Third, in terms of the ASEAN, it would be good to cluster like Busan (Singapore), Incheon Port (Tanjung Priok, Tanjung Perak, Manila, Tanjung Pelpas, Leam Chanbang, and Bangkok), and Gwangyang Port(Tanjung Priok, Tanjung Perak, Port Kang, Tanjung Pelpas, Leam Chanbang, and Bangkok). Third, Wilcoxon's signed-ranks test of models shows that all P values are significant at an average level of 0.852. It means that the average efficiency figures and ranking orders of the models are matched each other. The policy implication is that port policy makers and port operation managers should select benchmarking ports by introducing the models used in this study into the clustering of ports, compare and analyze the port development and operation plans of their ports, and introduce and implement the parts which required benchmarking quickly.

• Journal of the Korea Safety Management & Science
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• v.10 no.3
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• pp.127-135
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• 2008

In this study, we offered a way that is to make warehouse industry clustered in Incheon port for getting competitive and high end value added activities like advanced port logistics center considering trend that is changing functions of port and importance of port-hinterland. For this, we studied the existing research about port cluster, the present condition of warehouse industry in Incheon port and importance of value-added logistics activities. Also, we offered needs to build a high value-added and integrated logistics center by examples of advanced port logistics center in Singapore, Netherlands(Rotterdam) and Hongkong. We get the questionnaires for gathering ideas of port logistics industry about to set integrated logistics centers by strategy we offered that is making warehouse logistics clustered.

• Journal of Korea Port Economic Association
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• v.30 no.4
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• pp.151-168
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• 2014

The purpose of this paper is to show the brief efficiency and clustering measurement way by using the game cross-efficiency model which is newly introduced in this paper for 13 container ports during 3 years(2009, 2010, and 2013) with 3 input variables(depth, total area, and number of crane) and 1 output variable(container TEU). The main empirical results are as follows. First, the average rankings of game cross-efficiency model are Ningbo, Hongkong, Shanghai, Dubai, Singapore, Qingdao, Kaosiung, Busan, Tokyo, Incheon, Nagoya, Manila, Gwangyang ports in order. Second, according to ANOVA analysis, three models show the similar results in terms of the efficiency rankings. Third, in the clustering analysis using dendrogram, group A(Shangahi and Busan), group B(Ningbo and Nagoya), and group C(Incheon and Manila) show the common clustering ports during 3 or 2 years. The policy implication of this paper is that Korean port policy planner should introduce the game cross-efficiency method when measuring the individual port efficiency. Also port authority should consider the merits of the clustering ports for improving the port management and operations.

• Journal of Korea Port Economic Association
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• v.30 no.1
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• pp.23-55
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• 2014

The purpose of this paper is to show the clustering trend and to choose the clustering ports for 3 Korean ports(Busan, Incheon and Gwangyang Ports) by using the self organizing maps based on neural network(SOM) and Tier models for 38 Asian ports during 11 years(2001-2011) with 4 input variables(birth length, depth, total area, and number of crane) and 1 output variable(container TEU). The main empirical results of this paper are as follows. First, clustering results by using SOM show that 3 Korean ports[Busan(26.5%), Incheon(13.05%), and Gwangyang(22.95%) each]can increase the efficiency. Second, according to Tier model, Busan(Hongkong, Sanghai, Manila, and Singapore), Incheon(Aden, Ningbo, Dabao, and Bangkog), and Gwangyang(Aden, Ningbo, Bangkog, Hipa, Dubai, and Guangzhou) should be clustered with those ports in parentheses. Third, when both SOM and Tier models are mixed, (1) efficiency improvement of Busan Port is greater than those of Incheon and Gwangyang ports. (2) Incheon port has shown the slow improvement during 2001-2007, but after 2008, improvement speed was high. (3) improvement level of Gwangyang port was high during 2001-2003, but after 2004, improvement level was constantly decreased. The policy implication of this paper is that Korean port policy planner should introduce the SOM, and Tier models with the mixed two models when clustering among the Asian ports for enhancing the efficiency of inputs and outputs.

• Journal of Korea Port Economic Association
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• v.26 no.1
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• pp.43-60
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• 2010

The purpose of this paper is to show the clustering measurement way for Korean container terminals by using neural network based SOM(Self Organizing Map). Inputs[Number of Employee, Quay Length, Container Terminal Area, Number of Gantry Crane], and output[TEU] are used for 3 years(2002,2003, and 2004) for 8 Korean container terminals by applying both DEA and SOM models. Empirical main results are as follows: First, the result of DEA analysis shows the possibility for clustering among the terminals and reference terminals except Gamcheon and Gwangyang terminals because of the locational closeness. Second, the result of neural network based SOM clustering analysis shows the positive clustering in clustering positions 1, 2, 3, 4, and 5. Third, the results between SOM clustering and DEA clustering show the matching ratio about 67%. The main policy implication based on the findings of this study is that the port policy planner of Ministry of Land, Transport and Maritime Affairs in Korea should introduce the clustering measurement way for the Korean container terminals using neural network based SOM with DEA models for clustering Korean ports and terminals.

• Journal of Korea Port Economic Association
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• v.34 no.3
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• pp.17-52
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• 2018

The purpose of this paper is to measure the clustering change and analyze empirical results. Additionally, by using k-means, hierarchical, and mixed models on Asian container ports over the period 2006-2015, the study aims to form a cluster comprising Busan, Incheon, and Gwangyang ports. The models consider the number of cranes, depth, birth length, and total area as inputs and container twenty-foot equivalent units(TEU) as output. Following are the main empirical results. First, ranking order according to the increasing ratio during the 10 years analysis shows that the value for average linkage(AL), mixed ward, rule of thumb(RT)& elbow, ward, and mixed AL are 42.04% up, 35.01% up, 30.47%up, and 23.65% up, respectively. Second, according to the RT and elbow models, the three Korean ports can be clustered with Asian ports in the following manner: Busan Port(Hong Kong, Guangzhou, Qingdao, and Singapore), Incheon Port(Tokyo, Nagoya, Osaka, Manila, and Bangkok), and Gwangyang Port(Gungzhou, Ningbo, Qingdao, and Kasiung). Third, optimal clustering numbers are as follows: AL(6), Mixed Ward(5), RT&elbow(4), Ward(5), and Mixed AL(6). Fourth, empirical clustering results match with those of questionnaire-Busan Port(80%), Incheon Port(17%), and Gwangyang Port(50%). The policy implication is that related parties of Korean seaports should introduce port improvement plans like the benchmarking of clustered seaports.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.757-772
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• 2019

The purpose of this paper is to measure the clustering change and analyze empirical results, and choose the clustering ports for Busan, Incheon, and Gwangyang ports by using Artificial Neural Network, Social Network, and Tabu Search models on 38 Asian container ports over the period 2007-2016. The models consider number of cranes, depth, birth length, and total area as inputs and container throughput as output. Followings are the main empirical results. First, the variables ranking order which affects the clustering according to artificial neural network are TEU, birth length, depth, total area, and number of cranes. Second, social network analysis shows the same clustering in the benevolent and aggressive models. Third, the efficiency of domestic ports are worsened after clustering using social network analysis and tabu search models. Forth, social network and tabu search models can increase the efficiency by 37% compared to that of the general CCR model. Fifth, according to the social network analysis and tabu search models, 3 Korean ports could be clustered with Asian ports like Busan Port(Kobe, Osaka, Port Klang, Tanjung Pelepas, and Manila), Incheon Port(Shahid Rajaee, and Gwangyang), and Gwangyang Port(Aqaba, Port Sulatan Qaboos, Dammam, Khor Fakkan, and Incheon). Korean seaport authority should introduce port improvement plans by using the methods used in this paper.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2020.11a
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• pp.54-55
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• 2020

In 4th Industrial Revolution, technologies such as artificial intelligence, Internet of Things, and Big data are closely related to the maritime industry, which led to the birth of autonomous vessels. Due to the technical characteristics of the current vessel, the speed cannot be suddenly lowered, so complex communication such as the help of a tug boat, boarding of a pilot, and control of the vessel at the onshore control center is required to berth at the port. In this study, clustering analysis was used to resolve how to establish control criteria for vessels to enter port when autonomous vessels are operating. K-Means clustering was used to quantitatively stage the arrival pattern based on the accumulated AIS(Automatic Identification System) data of the incoming vessel, and the arrival phase using SOG(Speed over Ground), COG(Course over Ground), and ROT(Rate of Turn) Was divided into six phase.

• Journal of Korea Port Economic Association
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• v.24 no.4
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• pp.237-255
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• 2008

This paper aims to clarify if clustering effects among industries exist and if port-industry clustering effects exist. A knock-down approach was used in a survey and 16 industries were categorized. We defined which industry is more competitive in industry clusters and port-industry clusters. Another survey to experts was carried out to identify which industry is more appropriate to one of the three ports in Sudokwon (Seoul Metropolitan Areas): Incheon port, Pyungtaik port and Dangjin port. Five manufacturing industries are selected considering port-industry clustering relationships in this area and Analytic Hierarch Process was used for a pairwise comparison. Locational, social and economic factors are selected for 1st level. A result shows that Incheon port is more competitive in petroleum manufacturing, primary metal manufacturing and rubber and plastic manufacturing and Pyeontaik port is more competitive in metal assembly manufacturing and automobile and trailer manufacturing. However, sensitivity analysis shows a turnover of ranking in some industries. As there exist slight differences among three ports, cooperation is necessary when the government and Port Authorities make plans.