• Journal of Navigation and Port Research
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• v.26 no.2
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• pp.183-188
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• 2002

The forecast of a container traffic has been very important for port and development. Generally, Statistic methods, such as moving average method, exponential smoothing, and regression analysis have been much used for traffic forecasting. But, considering various factors related to the port affect the forecasting of container volume, neural network of parallel processing system can be effective to forecast container volume based on various factors. This study discusses the forecasting of volume by using the neural, network with back propagation learning algorithm. Affected factors are selected based on impact vector on neural network, and these selected factors are used to forecast container volume. The proposed the forecasting algorithm using neural network was compared to the statistic methods.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.4
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• pp.297-303
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• 2011

Major ports in Northeastern Asia engage in fierce competition to attract transshipment traffic volume. Existing time series analyses for analyzing port competition relationships examine the types of competition and relations through the signs of coefficients in cointegration equations using the transshipment traffic volume results. However, there are cases for which analyzing competing relationships is not possible based on the results of the transshipment traffic volume data differences and limitations in the forecasting of traffic volume. Accordingly, we used the Lotka-Volterra (L-V) model,also known as the ecosystem competitive relation model, to analyze port competition relations for the long-term forecast of South Korean transshipment traffic volume.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.5
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• pp.868-889
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• 2022

Fashion companies are using a big data approach as a key strategic analysis to predict and forecast sales. This study investigated the effectiveness of the past sales, web search volume, information amount, brand promotion, and the advertising endorser on the sales forecasting model. The study conducted the autoregressive distributed lag (ARDL) time series model using the internal and external social big data of a national fashion brand. Results indicated that the brand's past sales, search volume, promotion, and amount of advertising endorser information amount significantly affected the sales forecast, whereas the brand's advertising endorser search volume and information amount did not significantly influence the sales forecast. Moreover, the brand's promotion had the highest correlation with sales forecasting. This study adds to information-searching behavior theory by measuring consumers' brand involvement. Last, this study provides digital marketers with implications for developing profitable marketing strategies on the basis of consumers' interest in the brand and advertising endorser.

• Journal of Urban Science
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• v.7 no.1
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• pp.45-54
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• 2018

The purpose of this study is to find the factors that reduce prediction error in traffic volume using highway traffic volume data. The ARIMA model was used to predict the day, and it was confirmed that weekday and weekly characteristics were distinguished by prediction error. The forecasting results showed that weekday characteristics were prominent on Tuesdays, Wednesdays, and Thursdays, and forecast errors including MAPE and MAE on Sunday were about 15% points and about 10 points higher than weekday characteristics. Also, on Friday, the forecast error was high on weekdays, similar to Sunday's forecast error, unlike Tuesday, Wednesday, and Thursday, which had weekday characteristics. Therefore, when forecasting the time series belonging to Friday, it should be regarded as a weekly characteristic having characteristics similar to weekend rather than considering as weekday.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.6
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• pp.612-620
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• 2013

In this study, time series analysis was tried, which is widely applied to demand forecast of diverse fields such as finance, economy, trade, and so on, different from previous regression analysis. Future marine traffic volume was forecasted on the basis of data of the number of ships entering Incheon port from January 1996 to June 2013, through courses of stationarity verification, model identification, coefficient estimation, and diagnostic checking. As a result of prediction January 2014 to December 2015, February has less traffic volume than other months, but January has more traffic volume than other months. Also, it was found out that Incheon port was more proper to ARIMA model than exponential smoothing method and there was a difference of monthly traffic volume according to seasons. The study has a meaning in that future traffic volume was forecasted per month with time series model. Also, it is judged that forecast of future marine traffic volume through time series model will be the more suitable model than prediction of marine traffic volume with previous regression analysis.

• Journal of Navigation and Port Research
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• v.35 no.4
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• pp.343-349
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• 2011

The forecasting of container volume which is the basis of port logistics facilities expansion has a great influence on development of an port. Based on this importance, various previous studies have presented methodology on container volume forecasting. The results of many previous studies pointed out the limitations of future forecasting based on past container volume and emphasized that more various factors should be considered to compensate this. Taking notice of this point, this study forecasted future container volume by using ARIMA model, time series analysis and System Dynamics (SD) method, a dynamic analysis technique and performed the comparative review with the forecast of the Ministry of Land, Transport and Maritime affairs. Recently with rapid changes in economic and social environment, the non-linear change tendency for forecasting container traffic is presented as a new alternative to the country.

• Korean System Dynamics Review
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• v.15 no.3
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• pp.5-36
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• 2014

The purpose of this paper is to develop a System Dynamics model for estimating the volume of forest resources in the future and simulating the volume of function of public benefit linked to forest resources in dynamic manner. Also it is to analyze the impact when the volume of forest land conversion is controlled by policy using the SD model. The analysis was done at nation-wide for the simulation period 2000 to 2040. Estimated forest area was 6.2 million ha and estimated growing stock was $4.7\;billion\;m^3$ in 2040 from the future forecast without policies. Changing of forest resources, 13.9 billion tons of forest-ground-water storage was estimated, $1.8\;million\;m^3$ of erosion control of forest was estimated and 377 million tons of $CO_2$ absorption was estimated. As a result of simulation with two alternatives, forest area was less reduced and growing stock was bigger than do nothing policy. Also, function of public benefit reflected by changes of forest resources was enhanced. This study contributes to estimate the quantitatively measured volume of forest resources and function of public benefit over the 30 years in Korean forest land in scientific way. Using this SD model, decision maker would develop forest land policies more delicately for deserving forest resources and increasing the volume of function of public.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.3-6
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• 2003

In this study, we analyze the historical data of postal matter amount in korea, various social indexes concerning mail volume and postal data in developed countries. After correlation analysis between some variables, we suggest a new logistic model for forecasting the future mail volume. Finally, we forecast the mail volume in korea about 20 years hereafter.

• 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 Fisheries and Marine Sciences Education
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• v.26 no.1
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• pp.49-58
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• 2014

Variability factors of transship cargo in the container transportation market analysis short term factors. In the past, studies on the factor of variability in container cargo volume have focused on long term volume forecast and increase in investment and competitiveness from strategic perspectives. Unlike previous studies, this paper analyzes factors of variability in transshipment volume rapidly varying in short term and seeks measures. Since it was identified that transshipment volume depends on vessel operation cost and port volume in long term but effectively on special strategies launched by port authorities in short term, the port authority experienced rapid drop in volume should continue to observe strategies of competition ports and to make use of strategies seeking appropriate countermeasures.