• The Journal of Information Systems
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• v.33 no.2
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• pp.125-142
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• 2024

Purpose The purpose of this study is to identify and analyze the key factors influencing congestion in the in-out transportation at port container terminals, and to design of a predictive model for in-out congestion based on these analysis. This study focused on architecting a deep learning-based predictive model. Design/methodology/approach This study was conducted through the following methodology. First, hypotheses were established and data were analyzed to examine the impact of vessel schedules and external truck schedules on in-out transportation. Next, explored time series forecasting models to a design the architecture for deep learning-based predictive model. Findings According to the empirical analysis results, this study confirmed that vessel schedules significantly affect in-out transportation. Specifically, the volume of transportation increases as the vessel arrival/departure time and the cargo cutoff time approach. Additionally, significant congestion patterns in transportation volume depending on the day of the week and the time of day were observed.

• IE interfaces
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• v.22 no.1
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• pp.63-72
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• 2009

This paper considers a transporter scheduling problem under dynamic block transportation environment in shipbuilding. In dynamic situations, there exist the addition, cancellation or change of block transportation requirements, sudden breakdowns and maintenance of transporters. The transportation of the blocks in the shipyard has some distinct characteristics. Some blocks are available to be picked up at a specific time during the planning horizon while some other blocks need to be delivered before a specific time. These requirements cause two penalty times: 1) delay times incurred when a block is picked up after a required start time, and 2) tardy times incurred when a block shipment is completed after the required delivery time. The blocks are located at different areas in the shipyard and transported by transporters. The objective of this paper is to propose a heuristic algorithm based on a network flow model which minimize the weighted sum of empty transporter travel times, delay times, and tardy times. Also, a rolling-horizon scheduling method is proposed for dynamic block transportation environment. The performance of the proposed heuristic algorithms are evaluated through a simulation experiment.

• Journal of Korean Society of Transportation
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• v.17 no.5
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• pp.111-122
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• 1999

A service network design model for LTL freight transportation is formulated as a mixed integer Programming Problem with two heuristic solution a1gorithms. The Proposed model derives the transportation Path for each origination-destination pair, taking into account transportation cost over the links and handling costs over the nodes. The first algorithm searches for a local minimum solution from a given initial solution by improving the quality of solution repeatedly while the second a1gorithm searches for a better solution using Simulated Annealing Method. For both solution algorithms, the initial solution is derived by a modified reverse Diikstras shortest Path a1gorithm. An illustrative example, Presented in the last part of the Paper, shows that the proposed algorithms find solutions which reduce the cost by 12% and 15% respectively, compared to the initial solution.

• Journal of Forest and Environmental Science
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• v.32 no.2
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• pp.173-181
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• 2016

This study designed a model of the efficient production schemes and raw materials transportation optimization of current South Korean's simple and monolithic distribution system of wood to build a SCM (supply chain management) as a basic level to establish a distribution of future by pellet production of raw materials costs and reduce transport costs, and specifically to forest of pallet to contribute to revitalizing the market. The result of each transportation costs after building the best transportation network from raw material supply area to demand area applying transport law was 964,600 thousands Won from 6 supply areas to 7 demand areas. And the result of each model's analysis to get the pellet's efficient production through production cost reduction showed that it reduced from 325,701 Won/t to 240,106 Won/t, results of existing efficient pellet for the production model 8,233 tons over 20,000 tons annual production capacity from the size of the expanded production capacity when the expansion. However, when the production size expanded to 50,000 Tons of the production, the effect was very small even though production cost decreased.

• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.129-141
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• 2009

Intermodal freight transportation is defined as the movement of freight from origins to destinations by two or more transportation modes. When implemented in hub networks, it could enhance the efficiency of the networks because consolidated flows are transported by more suitable modes and technologies. In spite of this advantage, the intermodal hub network design problem has received limited attention in the literature partly because of the complex nature of the problem. This paper aims to develop an optimization model for designing intermodal hub networks with sin91e allocation strategy. The model takes into account various cost components of intermodal hub networks including transportation, stationary inventory, and service delay costs. Moreover, using transport frequency variables, it is capable of endogenously determining the transportation economies of scale achieved by consolidation of flows. As such, the model is able to realistically represent the characteristics of intermodal hub networks in practice. Since the model Is a complicated nonlinear integer programming problem, we perform model simplification based on the analytical study of the model, which could facilitate the development of solution algorithms in the future. We expect that this study contributes to the design of intermodal hub networks as well as to the assessment of existing logistics systems.

• Korean Management Science Review
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• v.13 no.1
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• pp.47-55
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• 1996

Rapid globalization of production and marketing functions makes choice of international transportation mode of great importance. In this paper, transportation mode is characterized by two factors, mean and variability of transportation lead time. We developed a simple mathematical model to estimate the relative impact of mean lead time, lead time variance and demand variance on the required average inventory level under specified service rates.

• Journal of Korean Society of Transportation
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• v.11 no.2
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• pp.5-26
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• 1993

In order to analyze travel mode choice behavior, behavioral models including logit model, based on revealed preference theory, have been using easily measurable variables such as individual socioeconomic characteristics and physical attributes of travel modes. But some recent attitudinal models of travel choice behavior have implied that the negligence of individual psychological variables and individual choice constraints in travel mode choice might preclude better prediction of individual travel mode choice behavior. In this context, this study was attempted to reconstruct an attitudinal model(AM), especially focused on the decision rules in travel mode choice decision making process, consistent with the conceptual framework relating individual attitude and choice constraints to choice behavior. And to evaluate the strengths of the AM to other comparative models(logit, linear-additive, conjunctive, lexicographic model) in predicting travel mode choice bebavior, an empirical study of the mode choice in work-trip to CBD in Seoul was performed. According to the results the percent of correct prediction(PCP) derived from the AM was higher than those derived from comparative models by at least 7 to 20% in predicting travel mode choice. But each model produced a different prediction accuracy depending on market segmentation by travel modal users, individual socioeconomic characteristics, transportation system characteristics, and satisfaction levels. The finding that different groups divided by a certain criterion employ different decision rules supports the necessity of developing a choice model such as the AM combining compensatory and noncompensatory decision rules, and suggests that a proposed transportation system management plan or policy may have different effects on each group.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.14-25
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• 2018

In recent years, it has become technically easier to explain factors related with traffic accidents in the Big Data era. Therefore, it is necessary to apply the latest analysis techniques to analyze the traffic accident data and to seek for new findings. The purpose of this study is to compare the predictive performance of the negative binomial regression model and the deep learning method developed in this study to predict the frequency of traffic accidents in expressways. As a result, the MOEs of the deep learning model are somewhat superior to those of the negative binomial regression model in terms of prediction performance. However, using a deep learning model could increase the predictive reliability. However, it is easy to add other independent variables when using deep learning, and it can be expected to increase the predictive reliability even if the model structure is changed.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.118-129
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• 2021

The sediment transportation caused by soil erosion due to rainfall-discharge in the large watershed scale plays critical role in human society. The relationship between rainfall-discharge-sediment transportation is depending on the start time of rainfall and end of rainfall but, the studies related with rainfall characteristics are insufficient. In this study, The Soil and Water Assession Tool (SWAT) model was used to study the relationship between rainfall-discharge-sediment transportation at the Sook river watershed which is monitored by the Ministry of Environment. To do this, first of all, the sensitivity analysis about model attributes was performed using monitored data. The accuracy analysis of SWAT model was conducted using the model's efficiency index (Nash and Sutcliffe model efficiency; NSE) and the coefficient of determination (R²). After that, it was studied what results could be obtained according to changes in rainfall timing and end points. In the result of discharge simulation, the modified rainfall values (sum of total rainfall starting time and end time) showed more high accuracy values (R²:0.90, NSE: 0.8) than original rainfall values (R²:0.76, NSE: 0.72). In the result of sediment transportation simulation, during calibration had more resonable results(R²:0.87, NSE: 0.86) than compared with original rainfall values (R²:0.44, NSE: 0.41). However, validation results of sediment transportation simulation showed low accuracy values compared with calibration results. This results maybe cause monitoring periods of sediment flow compared with discharge monitoring periods. Nevertheless, since rainfall characteristic plays critical rule in model results, continuous research on rainfall characteristic is needed.

• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.91-104
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• 2017

In this study, we discuss the modeling of domestic transport sector using GCAM(Global Change Assessment Model). The GCAM is one of integrated assessment models widely used in internationally modeling community, and applied for the evaluation of IPCC 5th Report. Nevertheless, it is noted that there are a considerable number of problems in its application to domestic transport sector. First, the base year information of GCAM for detailed transportation service demand is found not consistent with national statistics. Second, the transportation sector simulation results do not properly reflect the past trends of service demand. Thus, the base year service demand is carefully matched with the detailed national statistics. In addition, the existing models were checked and modified so that the simulation results of service demand can accurately reflect past trends of national statistics. As a result, it is reported in detail how the current GCAM simulation results are corrected and how the trend of past transportation sector service demands is properly reflected. This study is expected to be useful as a basic tool for future scenario analysis for transportation policy, technology evaluation and greenhouse gas reduction measures.