• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.3
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• pp.285-291
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• 2014

This study applied regression analysis to evaluate the impact of hourly average congestion calculated by bumper model in the congested area of each passage of each port on the peak time congestion, to suggest the model formula that can predict the peak time congestion. This study conducted regression analysis of hourly average congestion and peak time congestion based on the AIS survey study of 20 ports in Korea. As a result of analysis, it was found that the hourly average congestion has a significant impact on the peak time congestion and the prediction model formula was derived. This formula($C_p=4.457C_a+29.202$) can be used to calculate the peak time congestion based on the predicted hourly average congestion.

• Journal of Korea Port Economic Association
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• v.33 no.4
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• pp.135-148
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• 2017

This study fist raises the following research question. How does the port congestion affect port operational efficiency? To answer the question, this study adopts slacks based measure data envelopment analysis (SBM-DEA) model to analyze the efficiency of port considering the congestion. As a result of the DEA-CCR(Chanres, Cooper and Rhodes) model, both Busan(2011) and Ulsan(2011) are the most efficient decision making units(DMUs). As a result of the DEA-BCC(Banker, Chanrnes, and Cooper) model, Busan(2011), Ulsan(2011), Ulsan(2012), Busan(2012), and Yeosu Gwangyang(2012) are the most efficient DMUs. As a result of SBM-DEA model, Ulsan(2012), Busan(2011), Busan(2012), Incheon(2011), and Ulsan(2011) are the most efficient DMUs considering the port congestion. The result of DEA-CCR BCC model is not identical with the result of SBM-DEA model analysis. It means the port congestion does less affect the port operational efficiency. Should the number of the vessels with the port congestion minimize, Incheon and Yeosu Gwangyang port could save lots of the port congestion cost for a total of three years.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Journal of Korea Port Economic Association
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• v.19 no.2
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• pp.33-53
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• 2003

The purpose of this paper is to investigate the effectiveness of port investemnt which is one of the important elements for measuring the port efficiency by using congestion approach of DEA(Data Envelopment Analysis). Congestion is said to be present when increases in inputs result in input reductions. Congestion approach takes the forms of weak input disposability and strong input disposability. Empirical analysis by using congestion approach in this paper identified inefficiencies in the inputs including port investment, and indicated inefficient ports like the ports of Sokcho, Gunsan, Pohang, and Seoguipo which shows the large amount of slacks with congestion especially in terms of port investment. Therefore these ports should examine the reason about the inefficiency of port investment. The main policy implication based on the findings of this study is that The Ministry of Maritime Affairs & fisheries in Korea should introduce congestion approach when the amount of port investment to each port is decided.

• Proceedings of the Korea Port Economic Association Conference
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• 2003.07a
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• pp.221-242
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• 2003

The purpose of this paper is to investigate the effectiveness of port investment which is one of the important elements for measuring the port efficiency by using congestion approach of DEA(Data Envelopment Analysis). Congestion is said to be present when increases in inputs result in ouput reductions. Congestion approach takes the forms of weak input disposability and strong input disposability. Empirical analysis by using congestion approach in this paper identified inefficiencies in the inputs including port investment, and indicated inefficient ports like the ports of Sokcho, Gunsan, Pohang, and Seoguipo which shows the large amount of slacks with congestion especially in terms of port investment. Therefore these ports should examine the reason about the inefficiency of port investment. The main policy implication based on the findings of this study is that The Ministry of Maritime Affairs & Fisheries in Korea should introduce congestion approach when the amount of port investment to each port is decided.

• Asia pacific journal of information systems
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• v.26 no.3
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• pp.427-448
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• 2016

The stochastic phenomena of traffic network condition, such as traffic speed and density, are affected not only by exogenous traffic control but also by endogenous changes in service time during congestion. In this paper, we propose a mixed M/G/1 queuing model by introducing a condition-varying parameter of traffic congestion to reflect structural changes in the traffic network. We also develop congestion indices to evaluate network efficiency in terms of traffic flow and economic cost in traffic operating system using structure-changing queuing model, and perform scenario analysis according to various traffic network improvement policies. Empirical analysis using Korean highway traffic operating system shows that our suggested model better captures structural changes in the traffic queue. The scenario analysis also shows that occasional reversible lane operation during peak times can be more efficient and feasible than regular lane extension in Korea.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.359-365
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• 2005

This paper presents an reduced-order near-optimal controller for the congestion control of Available Bit Rate (ABR) service in Asynchronous Transfer Mode (ATM) networks. We introduce the model, of a class of ABR traffic, that can be controlled using a Explicit Rate feedback for congestion control in ATM networks. Since there are great computational complexities in the class of optimal control problem for the ABR model, the near-optimal controller via reduced-order technique is applied to this model. It is implemented by the help of weakly coupling and singular perturbation theory, and we use bilinear transformation because of its computational convenience. Since the bilinear transformation can convert discrete Riccati equation into continuous Riccati equation, the design problems of optimal congestion control can be reduced. Using weakly coupling and singular perturbation theory, the computation time of Riccati equations can be saved, moreover the real-time congestion control for ATM networks can be possible.

• Korean System Dynamics Review
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• v.12 no.1
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• pp.75-87
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• 2011

Congestion problems can be approached from the viewpoint of system dynamics theory. The relationship between road capacity and congestion can be explained by the 'relative control' archetype among four system archetypes suggested by Wolstenholme. There is a balancing feedback loop between road capacity and road congestion. However, there is another balancing loop between road congestion and car traffic volume, which keeps disrupting the equilibrium of the former loop. A system dynamics model, which is based on a partial adjustment model of induced traffic in the literature, is built to simulate three road building scenarios: 'Expanding investment', 'Balancing investment' and 'Frozen road investment' scenarios. The 'Expanding investment' scenario manages to drop congestion levels by 9% over 30 years, however, causing much higher emissions of $CO_2$ than other scenarios. The trade-off relationship between congestion levels and environmental costs must be taken into consideration for road investment policies.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.275-278
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• 2006

This paper describes a probabilistic annual congestion cost assessment of a grid at a composite power system derived from a model. This probabilistic congestion cost assessment simulation model includes capacity limitation and uncertainties of the generators and transmission lines. In this paper, the proposed probabilistic congestion cost assessment model is focused on an annualized simulation methodology for solving long-term grid expansion planning issues. It emphasizes the questions of "how should the uncertainties of system elements (generators, lines and transformers, etc.) be considered for annual congestion cost assessment from the macro economic view point"? This simulation methodology comes essentially from a probabilistic production cost simulation model of composite power systems. This type of model comes from a nodal equivalent load duration curve based on a new effective load model at load points. The characteristics and effectiveness of this new simulation model are illustrated by several case studies of a test system.

• International Journal of Highway Engineering
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• v.16 no.4
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• pp.127-134
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• 2014

PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.