• Journal of Digital Convergence
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• v.18 no.12
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• pp.87-95
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• 2020

The Incheon airport is a gateway to and from the Republic of Korea and has a great influence on the image of the country. Therefore, it is necessary to predict the number of airport passengers in the long term in order to maintain the quality of service at the airport. In this study, we compared the predictive performance of various time series models to predict the air passenger demand at Incheon Airport. From 2002 to 2019, passenger data include trend and seasonality. We considered the naive method, decomposition method, exponential smoothing method, SARIMA, PROPHET. In order to compare the capacity and number of passengers at Incheon Airport in the future, the short-term, mid-term, and long-term was forecasted by time series models. For the short-term forecast, the exponential smoothing model, which weighted the recent data, was excellent, and the number of annual users in 2020 will be about 73.5 million. For the medium-term forecast, the SARIMA model considering stationarity was excellent, and the annual number of air passengers in 2022 will be around 79.8 million. The PROPHET model was excellent for long-term prediction and the annual number of passengers is expected to be about 99.0 million in 2024.

• The Journal of Bigdata
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• v.6 no.2
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• pp.17-28
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• 2021

Current development of technologies related to 4^th industrial revolution and the pandemic of COVID-19 lead the rapid expansion of e-marketplace. The level of competition among several companies gets increased by introducing different strategies. To cope with the current change in the market and satisfy the customers who request the better delivery service, the new concept, fulfillment, has been introduced. It makes the leadtime of process from order picking to delivery reduced and the efficiency improved. Still, the efficiency of operation in fulfillment centers constrains the service level of the entire delivery process. In order to solve this problem, several different approaches for demand forecasting and coordinating supplies using Bigdata, IoT and AI, which there exists the trivial limitations. Because it requires the most lead time for operation and leads the inefficiency the process from picking to packing the ordered items, the logistics service providers should try to automate this procedure. In this research, it has been proposed to develop the efficient plans to automate the process to move the ordered items from the location where it stores to stage for packing using AGV and AMR. The efficiency of automated devices depends on the number of items and total number of devices based on the demand. Therefore, the result of simulation based on several different scenarios has been analyzed. From the result of simulation, it is possible to identify the several factors which should be concerned for introducing the automated devices in the fulfillment centers. Also, it can be referred to make the optimal decisions based on the efficiency metrics.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.477-483
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• 2010

The existing mass transport system, with its limited capacity and the saturated road networks, has given cause for a new means of transport to be developed, and strong demands for such new means of transport are observed more than ever. However, the traffic authority is seeking a new transport system that focuses more on LRT(Light Rail Transit), a downsized version of the existing urban railroad, rather than one that is appropriate to solve the traffic problems. Moreover, local governments are experiencing difficulties in planning their own mass transportation(bus or urban railroad) as they have no specified criteria for selecting a mass transport system. Accordingly, there has been an increasingly loud voice that calls for criteria to determine which mass transport system befits each transportation corridor. This paper develops a mass-transport demand forecasting model based on the GIS Buffering analysis of each transportation corridor in the city, sets up the capacity for each mass transport system and presents the criteria for selecting an optimal mass transport system for each transportation corridor. It also presents a methodology that identifies necessary and sufficient conditions for selection and evaluation, since it is most important to select the optimal mass-transport system that can meet the demand by each mass-transportation corridor.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1799-1810
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• 2011

Origin-Destination(OD) trip survey being used in travel demand forecasting has been obtained through totalizing process with direct sample survey techniques such as plate license survey, roadside interview, household travel survey, and cordon line counts. However, the OD survey has many discrepancies in sampling, totalizing process, and such discrepancies contains problems of difference between forecasted traffic volume and observed data. On the other hand, transit smart card data recently collected has credible resource of obtaining travel information for bus and metro. This paper presents parameter estimation of gravity model by using transit smart card data. Through the parameter estimation method, we estimated =0.57, ${\beta}$=0.14 of gravity model for bus, and ${\alpha}$=-0.21, ${\beta}$=0.05 for metro. The statistical test such as T-test, coefficient of correlation, Theil`s inequality coefficient showed no difference between observed volume and estimated volume. Elasticities of bus and metro derived in this paper are also reasonable.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1835-1847
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• 2011

After conducting demonstration of transportation card for bus from Godeok-dong to Sangil-dong in 1996, due to continuously support for public transportation, the transportation card utilization of metropolitan public transportation passengers is currently going beyond 90%. In the current situation, transportation information system is mainly focused on road operation and control, offer of real time information and research of transportation information system using transportation card data which is differentiated by previous transportation information system is needed to study. This paper compare and analyze transportation information system, which is being used to each country, based on foreign examples of activating use of transportation card then introduce figure of advanced transportation information system which provide decision making feature for improving policy and institution of public transportation based on transportation card data, scientific analysis of passenger information, information of demand forecasting, variation and so on for constructing new route.

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.22-28
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• 2013

This study aims to propose the location selection method of car-sharing services in Daejeon Metropolitan city. In order to select locations for car-sharing, Daejoen area was divided in $500m{\times}500m$ cell size using GIS Arc/Info 10, and input factors which may affect car-sharing service were determined, and then each input factor was standardized for analysis. The weight for each input factor was determined through experts' survey and index of goodness of fit was estimated in each cell ($500m{\times}500m$ size) using AHP method. Also, This study proposed the method to select 30 service facility location using Location-allocation Model in Network Analysis module. The proposed method for the location selection of car-sharing service in this study can be used for preliminary data for initial car-sharing introduction. Henceforward, appropriate demand forecasting and economic evaluation for the location selection of car-sharing service are needed for the further study.

• Ocean and Polar Research
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• v.42 no.3
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• pp.211-223
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• 2020

With the increasing demand for improved marine environments and safety, greater ability to minimize damages to coastal areas from harmful organisms, ship accidents, oil spills, etc. is required. In this regard, an accurate assessment and understanding of current systems is a crucial step to improve forecasting ability. In this study, we examine spatial and temporal characteristics of current systems in the adjacent seas of Jeju Island using a high-resolution regional ocean circulation model. Our model successfully captures the features of tides and tidal currents observed around Jeju Island. The tide form number calculated from the model result ranges between 0.3 and 0.45 in the adjacent seas of Jeju Island, indicating that the dominant type of tides is a combination of diurnal and semidiurnal, but predominantly semidiurnal. The spatial pattern of tidal current ellipses show that the tidal currents oscillate in a northwest-southeast direction and the rotating direction is clockwise in the adjacent seas of Jeju Island and counterclockwise in the Jeju Strait. Compared to the mean kinetic energy, the contribution of tidal current energy prevails the most parts of the region, but largely decreases in the eastern seas of Jeju Island where the Tsushima Warm Current is dominant. In addition, a Lagrangian particle-tracking experiment conducted suggests that particle trajectories in tidal currents flowing along the coast may differ substantially from the mean current direction. Thus, improving our understanding of tidal currents is essential to forecast the transport of marine pollution and harmful organisms in the adjacent seas of Jeju Island.

• Journal of Korean Society of Transportation
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• v.23 no.8 s.86
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• pp.139-145
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• 2005

`Influence Area` means the area that the traffic flow pattern of which is changed remarkably after the construction of highway facilities. The Influence area would be a significant criteria for demand forecasting and economic analysis. However existing methods for defining influence area such as O/D method, traffic volume variation method and rate of traffic volume variation method have no standard criteria. In this paper, some problems which existing methods have are analyzed and a new method travel time method - is introduced. Influence area can be induced as the area where the vehicles reach from starting traffic zone to ending zone to which 95 percentile vehicles want to travel. In addition, the value of standard criteria for defining influence ayes are induced via this method.

• Journal of Korean Society of Transportation
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• v.24 no.1 s.87
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• pp.7-18
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• 2006

Nowadays, the movement of commercial vehicles has caused severe congestion, environmental problems and damaged pavements in urban area. To solve the Problems, this research has proposed a methodology to build-up a freight network that is enable to manage the movement and demand of commercial vehicles. The network based on urban arterial network has developed according to freight traffic volume that is under the influence of road types among The in study area. Freight network has been developed by assignment method of 4-step transportation forecasting. Once, acceptable limits of freight traffic volume are decided, freight network can be developed using the capacity To confirm the application of the methodology, freight network of Seoul city has developed by the methodology.