• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.320-322
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• 2018

Data analysis is a process of generating useful information by evaluating real-world raw data for making better decisions in business development. In the freight transport logistics companies, the analysis of freight data is increasingly garnering considerable importance among the users for making better decisions regarding freight cost reductions. Consequently, in this study, we used R programming language to analyze the freight data that are collected from freight transport logistics company. Usually, the freight rate varies based on chosen day of the week. In here, we analyzed and visualized the results such as frequency of cost vs days, frequency of requested goods in ton vs days, frequency of order vs days, and frequency of order status vs days for the last one-year freight data. These analysis results are beneficial in the viewpoint of the users in ordering process.

• Journal of the Korean Society for Railway
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• v.13 no.3
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• pp.327-332
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• 2010

In this paper we presents an approach to assessing the efficiency of freight railroad stations reflecting freight item importance weights with multiple performance indicators and multiple operational condition indicators. We evaluate 187 freight railroad stations using data envelopment analysis with assurance region. Each freight item's loading/unloading volume is used as a performance indicator. Freight labor and yard capacity are used as operational condition indicators. Freight item importance weights are reflected to the data envelopment analysis as assurance region. The evaluation results facilitates the organization's decision making by providing valuable information.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.377-393
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• 2020

Freight management systems require a new business model for rapid decision making to improve their business processes by dynamically analyzing the previous experience data. Moreover, the amount of data generated by daily business activities to be analyzed for making better decisions is enormous. Online-to-offline or offline-to-online (O2O) is an electronic commerce (e-commerce) model used to combine the online and physical services. Data analysis is usually performed offline. In the present paper, to extend its benefits to online and to efficiently apply the big data analysis to the freight management system, we suggested a system architecture based on O2O services. We analyzed and extracted the useful knowledge from the real-time freight data for the period 2014-2017 aiming at further business development. The proposed system was deemed useful for truck management companies as it allowed dynamically obtaining the big data analysis results based on O2O services, which were used to optimize logistic freight, improve customer services, predict customer expectation, reduce costs and overhead by improving profit margins, and perform load balancing.

• Journal of the Korean Society for Railway
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• v.4 no.3
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• pp.116-122
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• 2001

As freight traffic becomes heavier, the high speed of existing freight cars is essential, instead of the construction of a new railway. The high speed can be achieved by the modifications of freight bogie design. In this paper, an analytical model of freight bogie is developed to decide the critical speed. The dynamic responses of the analytical model are compared with the experimental data from a running test of freight bogie and showed good agreements between them. The analytical model is used to find the design of freight bogie. The parameter studies show that the reduction of wheelset mass ratio and the increase of the axle distance of freight bogie can increase the critical speed, but the primary lateral stiffness has little effects on the critical speed. And this study also shows that smaller wheel conicity deteriorates the running safety of freight car, which means that the overhauling of the wheel of freight bogie should be done regularly.

• Proceedings of the KSR Conference
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• 2001.10a
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• pp.179-186
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• 2001

As the freight traffic becomes heavier, the high speed of existing freight cars is essential instead of the construction of a new railway. The high speed can be achieved by the design modifications of the freight bogie. In this paper, an analytical model of freight bogie including the lateral force between rail and the flange of wheel is developed to decide the critical speed, which activates a hunting motion and tells the running safety of freight bogie. The dynamic responses of the analytical model were compared with an experimental data from a running test of a freight bogie and showed good agreements between them. The analytical model is used to find the design modifications of the freight bogie by parameter studies. The results show that the reduction of wheelset mass ratio and the increase of the axle distance of the freight bogie can increase the critical speed, but the primary lateral stiffness has little effects on the critical speed. And this also study shows that smaller wheel conicity deteriorates the running safety of the freight car, which means the overhauling of the wheel of freight bogie should be done regularly.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.2
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• pp.166-174
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• 2005

Plan of Logistics facility and Management of Freight trucks need for Efficient Improvement of Urban Goods Movement. For this, it need to clear flow pattern of Freight volumes and Freight trucks on urban space. Therefor, The purpose of this study is to clear space flow pattern of Freight volumes and Freight trucks as base data for Plan of Urban Goods Movement on Incheon city. Incheon city is selected because it is at sea & air ports and carries out various Activity of Urban Goods Movement. As the result of this study, it understands and analyzes Characteristic on flow pattern of Freight volumes and Freight trucks. Through this study, we'll expect that this results could be contributed in the understand of actual conditions of Freight volume and freight trucks and the basic data for Improvement of urban goods movement and the management policy of freight trucks in urban goods movement.

• Journal of Distribution Science
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• v.12 no.2
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1542-1548
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• 2010

Transportation cost takes a major portion on logistics cost. The reduction of transportation cost is a key issue to achieve national competitiveness and logistics cost reduction. And railway must play a important role to shift from road freight transport to environment-friendly transport. There are many idle freight cars in railway freight transportation and they give rise to inefficient operation of freight car. It is well known that the main reason of idle freight cars is unbalanced demand according to direction. In this study, we analysed the current status on idle freight cars in railway at the level of daily data. This results help follow-up research to cut down idle freight cars in railway.

• Journal of Korean Society of Transportation
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• v.24 no.4 s.90
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• pp.103-113
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• 2006

Market Segmentation for Freight Transportation has been used to know the strategies both efficiency of freight transportation market and attraction of freight volume for carriers. It was so difficult to understand the individual preference of shippers that all shippers could be only homogenized through market segmentation. In Korea. standard Industrial classification has been used for freight market segmentation. This study evaluated another new market segmentation method for manufacturing industry. From the study, we knew that the best relevant market segmentation criterion was annual input-output volume, which showed excellent segmenting ability. Also. the results showed many differences against segmentation results according to standard industrial classification. This study had a meaning as a new trial which segmented freight transportation market using company size and shipment characteristic data.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.167-175
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• 2002

The objective of this study is to reveal a shipper's preference for road freight transport according to commodity classification code. The shipper's preference in freight transport can be obtained by using value of travel-time savings. The characteristics of freight are so various that the shipper's preference also appear widely different. In these days, there were few attempts to estimate value of freight travel-time savings in Korea. but most of them included only rail or marine freight transport so it couldn't obtain unique travel-time savings for road freight transport. In this study the value of travel-time savings of road freight transport was estimated according to commodity classification code. Revealed preference method and associated binominal logit models were applied to estimate the value of travel-time savings in transit from a Seoul metropolitan commodity flow survey data in 1998. Data sets were segmented by commodity classification code and nineteen binominal legit models were estimated according to segmented groups. The results of this study showed that the value of freight travel-time savings varied wide ranges from 16,441 won to 66,769 won per hour a vehicle along with commodity classification code.