• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.132-145
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• 2022

This study was based on the black box images of traffic accidents on highways, cluster analysis and prediction model comparisons were carried out. As analysis data, vehicle driving behavior and road surface conditions that can grasp road and traffic conditions just before the accident were used as explanatory variables. Considering that traffic accident data is affected by many factors, cluster analysis reflecting data heterogeneity is used. Each cluster classified by cluster analysis was divided based on the ratio of the severity level of the accident, and then an accident prediction evaluation was performed. As a result of applying the Logit model, the accident prediction model showed excellent predictive ability when classifying groups by cluster analysis and predicting them rather than analyzing the entire data. It is judged that it is more effective to predict accidents by reflecting the characteristics of accidents by group and the severity of accidents. In addition, it was found that a collision accident during stopping such as a secondary accident and a side collision accident during lane change act as important driving behavior variables.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.380-385
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• 2019

This paper is concerned with departure passengers' dwell time analysis using real system data. Previous researches emphasize the importance of dwell time analysis from perspective of airport terminal planning and non-aeronautical revenue. However, short-term airport operation using passengers' dwell time is considered impossible due to absence of passengers' behavior data. Recently, in accordance with the wave of smart airport, world leading airports are systematically collecting passenger data. So there is high possibility of analyzing passengers' dwell time with the data stacked in the airport database. We conducted dwell time analysis using data from Incheon Int'l airport. In order to handle passenger data, we adapted clustering algorithm which is one of data mining techniques. As a clustering result, passengers are divided into 3 clusters. One is the cluster for passengers whose dwell time is relatively short and who tend to spend longer time in the airside. Another is the cluster for passengers who have near 3 hours dwell time. The other is the cluster for passengers whose total dwell time is extremely long.

• Journal of Korea Multimedia Society
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• v.17 no.10
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• pp.1160-1170
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• 2014

Clustering methods are very useful in many fields such as data mining, classification, and object recognition. Both the supervised and unsupervised grouping approaches can classify a series of sample data with a predefined or automatically assigned cluster number. However, there is no constraint on the number of elements for each cluster. Numbers of cluster members for each cluster obtained from clustering schemes are usually random. Thus, some clusters possess a large number of elements whereas others only have a few members. In some areas such as logistics management, a fixed number of members are preferred for each cluster or logistic center. Consequently, it is necessary to design a clustering method that can automatically adjust the number of group elements. In this paper, a k-means based clustering method with a fixed number of cluster members is proposed. In the proposed method, first, the data samples are clustered using the k-means algorithm. Then, the number of group elements is adjusted by employing a greedy strategy. Experimental results demonstrate that the proposed clustering scheme can classify data samples efficiently for a fixed number of cluster members.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.345-354
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• 2021

With the evolution of containerization and globalization of supply chains, aspects of port functions have made the transition from the sea to the inland region that forms the dry port. To explore the relationship between dry ports and regional economic development, this study uses a gravity model and forecast model to analyze 1,040 observations in 104 cities (22 dry port cities) along the Yangtze River Economic Belt (YREB) from 2008 to 2017. The model includes economic variables, logistics variables, foreign relations variables, and human capital variables. It was found that the dry port is positively correlated with trade volume. Compared with a city without a dry port, the trade volume of a city with a dry port will increase 0.099 times. It can be concluded that a dry port is crucial for the economic development of the YREB. It was also found that per capita GDP as an economic variable, road area and rail number as logistics variables, and foreign relation variables are positively correlated with trade volume, while the human capital variable has no significant effect on trade volume. In addition, governmental policy implications are addressed from the aspects of dry port and industry cluster caused by foreign investment.

• Journal of the Korea Society of Computer and Information
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• v.23 no.5
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• pp.9-14
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• 2018

For an efficient management of electricity market and power systems, accurate forecasts for electricity demand are essential. Since there are many factors, either known or unknown, determining the realized loads, it is difficult to forecast the demands with the past time series only. In this paper we perform a cluster analysis on electricity demand data collected from Jan. 2000 to Dec. 2017. Our purpose of clustering on electricity demand data is that each cluster is expected to consist of data whose latent variables are same or similar values. Then, if properly clustered, it is possible to develop an accurate forecasting model for each cluster separately. To validate the feasibility of this approach for building better forecasting models, we clustered data with t-SNE. To apply t-SNE to time series data effectively, we adopt the dynamic time warping as a similarity measure. From the result of experiments, we found that several clusters are well observed and each cluster can be interpreted as a mix of well-known factors such as trends, seasonality and holiday effects and other unknown factors. These findings can motivate the approaches which build forecasting models with respect to each cluster independently.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.443-450
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• 2021

The study is devoted to the formation of a economic principles cargo delivery management in global supply chains. Mathematical model of delivering special categories of goods by road is a key element of these principles. The article analyzes the existing studies on solving the problem of cargo delivery in various aspects. It was noted that the greatest attention is paid to legal regulation, last mile delivery, optimization of routes and delivery schemes, information support, technological innovations, cluster routing, etc. In the developed mathematical model a minimum of total costs of forming loading units and freight shipments was defined as the criterion of optimality of organizing delivery by motor transport. The authors propose the creation of logistics clusters allowing the integration of urban transport flows and global supply chains.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.06a
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• pp.183-185
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• 2014

The purpose of this paper aims at contributing to the national economic development through global competitiveness enhancement by marine finance's hub and marine logistics cluster by finance specialization and finance support as a creative-type service industry in global shipping port logistics. This study adopted the integrated approach and applied it to policy implementation to achieve the effectiveness. Creative-type marine finance development stages as a tool of policy implementation and the guide line for the time of policy implementation are followed by Stage 1(Construction & Growth Policy) for 2013~2016, Stage 2(Forstering & Activation Policy) for 2017~2019) and Stage 3(Continuous Development Policy) after 2020 until its completion. Korea has the inferiority over the competitiveness in global marine finance and needs a strategic approach to secure the liquidity of marine finance; interim, Islamic finance has been come to the force as a new alternative in financial transaction being accompanied by a spot transaction since the crisis of global finance. In order to create a potential slack of Korea in marine finance practice, in addition, this study suggests a consortium with the circle of Islamic finance as a clue of an easier policy implementation at the beginning stage.

• Korea Trade Review
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• v.46 no.5
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• pp.65-81
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• 2021

The technological development in the era of the 4th industrial revolution is changing the paradigm of various industries. Various technologies such as big data, cloud, artificial intelligence, virtual reality, and the Internet of Things are used, creating synergy effects with existing industries, creating radical development and value creation. Among them, the logistics sector has been greatly influenced by quantitative data from the past and has been continuously accumulating and managing data, so it is highly likely to be linked with big data analysis and has a high utilization effect. The modern advanced technology has developed together with the data mining technology to discover hidden patterns and new correlations in such big data, and through this, meaningful results are being derived. Therefore, data mining occupies an important part in big data analysis, and this study tried to analyze data mining techniques that can contribute to the logistics field and common logistics using these data mining technologies. Therefore, by using the AHP technique, it was attempted to derive priorities for each type of efficient data mining for logisticalization, and R program and R Studio were used as tools to analyze this. Criteria of AHP method set association analysis, cluster analysis, decision tree method, artificial neural network method, web mining, and opinion mining. For the alternatives, common transport and delivery, common logistics center, common logistics information system, and common logistics partnership were set as factors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5865-5872
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• 2011

Multi-hop communication in clustering system is the technique that forms the cluster to aggregate the sensing data and transmit them to base station through midway cluster head. Cluster head around base station send more packet than that of far from base station. Because of this hot spot problem occurs and cluster head around base station increases energy consumption. In this paper, I propose a cluster group head selection using trajectory clustering technique(CHST). CHST select cluster head and group head using trajectory clustering technique and fitness function and it increases the energy efficiency. Hot spot problem can be solved by selection of cluster group with multi layer and balanced energy consumption using it's fitness function. I also show that proposed CHST is better than previous clustering method at the point of network energy efficiency.

• Journal of Navigation and Port Research
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• v.34 no.10
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• pp.823-831
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• 2010

The shipping business industries which are the transport and logistics intensive in Busan, have been small sized and scattered in many areas, that is why they could not create a synergy effect utterly. Because of the need to develop the shipping business cluster complex in Busan in order to concentrate those industries and attract high value added firms, this study tries to suggest an approach to build the cluster. Firstly, how various shipping business related firms in Busan and capital area demand the cluster complex are searched through questioning survey. Secondly, the gradual scheme to integrate lots of business companies, governmental authorities and educational institutes and global strategy to invite domestic and foreign organizations in Myeong Ji area near to Busan New Port. Thirdly, the expected economic benefits of the cluster construction are calculated quantitatively.