• KIEE International Transactions on Power Engineering
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• v.11A no.4
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• pp.21-26
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• 2001

As competition is introduced in the electricity supply industry, congestion becomes a more important issue. Congestion in a transmission network occurs due to an operating condition that causes limit violations on the transmission capacities. Congestion leads to inefficient use of the system, or causes additional costs (Congestion cost). One way to reduce this inefficiency or congestion cost is to control the transmission flow through the installation of UPFC (Unified Power Flow Controller). This paper also deals with an optimal siting of the UPFC for reducing congestion cost by using shadow prices. A performance index for an optimal siting is defined as a combination of line flow sensitivities and shadow prices. The proposed algorithm is applied to the sample system with a condition, which is concerning the quadratic cost functions. Test results show that the siting of the UPFC is optimal to minimize the congestion cost by the proposed algorithm.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.12
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• pp.93-98
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• 2012

Economics of available alternatives in the transmission planning are evaluated by the investment cost, loss cost and congestion cost. Congestion/loss cost is calculated in many years and many load levels by unit commitment of generators, optimal dispatch, load flow, judgement about transmission congestion and re-dispatch to reduce the congestion. The greatest difficulties to introduce variable optimization techniques on the transmission planning is the convergence of load flow. In this paper, economics in the transmission planning are evaluated using DC load flow, and case study is conducted on the Korea power system by proposed congestion/loss calculation methods.

• International Journal of Highway Engineering
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• v.18 no.3
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• pp.87-94
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• 2016

PURPOSES : This study analyzed the lane-by-lane traffic flow characteristics in Korea by using real-world data, including congestion levels, for 2-, 3-, and 4-lane freeways. METHODS : On the basis of a literature review, lane flow and speed characteristics were analyzed using flow measurements and speed ratios. In addition, the effect of congestion levels on traffic flow were visualized using rescaled cumulative plots. RESULTS : Driver behavior varied depending on the congestion level. During free-flow conditions, the lane-use ratio of individual lanes varied largely, whereas during congestion, the ratio was nearly the same for all lanes (i.e., equilibrium). During maximum-flow and congestion conditions, the median lane was used more than the shoulder lane, whereas during all other conditions, the shoulder lane had a higher lane-use ratio. In 3- or 4-lane freeways, the lane-use ratio of the median lane always exceeded 1 and was the highest during free-flow conditions. CONCLUSIONS : The results of the present analysis can be used as an index to predict congestion before a lane is overcapacitated. Moreover, the results can be applied in variable lane guidance systems, such as car navigation systems and variable message displays, to control traffic flow.

• 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.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1359-1363
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• 2009

The object of this research is to develop a simulation system for earthmoving operations in consideration of the impact of congestion in-between equipment and existing traffic flow around the site. The congestion in-between equipment and traffic flow affect work productivity. The conventional discrete event simulation, however, has limitations in simulating the flow of construction equipment. To consider the impact of congestion in-between equipment and existing traffic flow, in this paper, a multi-agent based simulation model that can realize characteristics of truck behavior more accurately to consider the impact of congestion was proposed. In this simulation model, multiple agents can identify environmental changes and adapt themselves to the new environment. This modeling approach is a better choice for this problem since it describes behavioral characteristics of each agent by sensing changes in dynamic surroundings. This study suggests a detailed system design of the multi-agent based simulation system.

• 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.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.101-104
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• 2000

Nowadays, the issue of congestion control in high-speed communication networks becomes critical in view of the bandwidth-delay products for efficient data flow. In particular, the fact that the congestion is often accompanied by the data flow from the high-speed link to low-speed link is important with respect to the stability of closed-loop congestion control. The Virtual-Connection Network (VCN) in Gigabit Ethernet networks is a packet-switching based network capable of implementing cell- based connection, link-by-link flow-controlled connection, and single- or multi-destination virtual connections. VCN described herein differ from the virtual channel in ATM literature in that VCN have link-by-link flow control and can be of multi-destination. VCNs support both connection-oriented and connectionless data link layer traffic. Therefore, the worst collision scenario in Ethernet CSMA/CD with virtual collision brings about end-to-end delay. Gigabit Ethernet networks based on CSMA/CD results in non-deterministic behavior because its media access rules are based on random probability. Hence, it is difficult to obtain any sound mathematical formulation for congestion control without employing random processes or fluid-flow models. In this paper, an analytical method for the design of a congestion control scheme is proposed based on Smith＇s principle to overcome instability accompanied with the increase of end-to-end delays as well as to avoid cell losses. To this end, mathematical analysis is provided such that the proposed control scheme guarantees the performance improvement with respect to bandwidth and latency for selected network links with different propagation delays. In addition, guaranteed bandwidth is to be implemented by allowing individual stations to burst several frames at a time without intervening round-trip idle time.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.107-109
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• 2000

This paper presents an algorithm for reducing congestion cost using decoupled optimal power flow. The main idea of this algorithm is to reduce the reactive power flows on the congested lines in reactive power optimization. New objective function for reducing congestion cost is proposed in the reactive formulation by introducing the shadow prices for congested lines. The proposed algorithm is tested for IEEE 14-bus sample system, and the results are presented and discussed.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.820-831
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• 2015

In a deregulated electricity market, congestion of the transmission lines is a major problem the independent system operator (ISO) would face. Rescheduling of generators is one of the most practiced techniques to alleviate the congestion. However, not all generators in the system operate deterministically and independently, especially wind power generators (WTGs). Therefore, a novel optimal rescheduling model for congestion management that accounts for the uncertain and correlated power sources and loads is proposed. A probabilistic power flow (PPF) model based on 2m+1 point estimate method (PEM) is used to simulate the performance of uncertain and correlated input random variables. In addition, the impact of direct electricity purchase contracts on the congestion management has also been studied. This paper uses artificial bee colony (ABC) algorithm to solve the complex optimization problem. The proposed algorithm is tested on modified IEEE 30-bus system and IEEE 57-bus system to demonstrate the impacts of the uncertainties and correlations of the input random variables and the direct electricity purchase contracts on the congestion management. Both pool and nodal pricing model are also discussed.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.2
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• pp.36-46
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• 2020

This study is designed to help customers use more comfortable airports by predicting congestion and congestion times by identifying the traffic routes of passengers in the airport building by day of the week and time by using Wi-Fi sensor collectors, one of the IoT technologies. Analysis of passenger traffic analysis data showed that the most congested time zones were from noon. to 2p.m. for all facilities, which could be used to improve major facilities. Regression analysis of factors affecting congestion found that self-check-in reduces congestion and check-in counters increases congestion. These findings will provide important implications for operations, including congestion management at airports.