• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.181-190
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• 2022

In this paper, we use a LiDAR sensor and an image camera to detect a left-turning waiting vehicle in two ways, unlike the existing image-type or loop-type left-turn detection system, and a left-turn traffic signal corresponding to the waiting length of the left-turning lane. A system that can efficiently assign a system is introduced. For the LiDAR signal transmitted and received by the LiDAR sensor, the left-turn waiting vehicle is detected in real time, and the image by the video camera is analyzed in real time or at regular intervals, thereby reducing unnecessary computational processing and enabling real-time sensitive processing. As a result of performing a performance test for 5 hours every day for one week with an intersection simulation using an actual signal processor, a detection rate of 99.9%, which was improved by 3% to 5% compared to the existing method, was recorded. The advantage is that 99.9% of vehicles waiting to turn left are detected by the LiDAR sensor, and even if an intentional omission of detection occurs, an immediate response is possible through self-correction using the video, so the excessive waiting time of vehicles waiting to turn left is controlled by all lanes in the intersection. was able to guide the flow of traffic smoothly. In addition, when applied to an intersection in the outskirts of which left-turning vehicles are rare, service reliability and efficiency can be improved by reducing unnecessary signal costs.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.4
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• pp.181-192
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• 2003

Increased vehicles on the restricted road, the conventional traffic light to losses the function of optimal cycle. The conventional traffic light dose not consider passenger car unit ,offset, and length of traffic intersection. As a result, 30~45% of conventional traffic cycle does not match the present traffic cycle. In this paper, we study the disard vantage of conventional traffic light and improve the vehicle average waiting time in the traffic intersection and vehicle average speed using fuzzy logic. Moreover, it will be able to forecast the optimal traffic information, road under construction and dangerous road using internet.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.292-297
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• 2000

This paper proposes a new concept of coordinating green time which controls 10 traffic intersection systems. For instance, if we have a baseball game at 8 pm today, traffic volume toward the baseball game at 8 pm today, traffic volume toward the baseball game will be increased 1 hour or 1 hour and 30 minutes before the baseball game. At that time we can not predict optimal green time Even though there have smart elctro-sensitive traffic light system. Therefore, in this paper to improve average vehicle speed and reduce average vehicle waiting time, we created optimal green time using fuzzy rules and neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dosen't consider coordinating green time.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.2
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• pp.237-253
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• 2017

There are many systematic approaches to manage Interlibrary loan (ILL) efficiently. In this paper, we propose an operating method reducing user-waiting in ILL. Users are usually informed to wait around two to three days in a regional ILL service. The reason of the waiting is that there is inefficiency in running material-delivery vehicles. A mixed integer programming is employed to resolve this issue. It provides an optimal vehicle-route maximizing material-deliveries subject to time availability. We used an actual data of a regional ILL system to provide optimal vehicle routes under different time settings.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.4
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• pp.51-65
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• 2012

Battery switching EVs are considered to be a promising alternative to overcome long charging time problems in the EV adoption. The purpose of this research is to develop integrated model for battery inventory and battery switching waiting times. Due to complexity of exact analysis, a practical approximation method is developed, which provides close results to exact values. Numerical experiments show that there exist trade-offs between the battery inventory level and the number of switching stations. The proposed model can be applied to evaluate and select the minimum cost alternative in implementing the battery switching stations.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.23-30
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• 2022

Recently, traffic congestion and environmental pollution have occurred due to population concentration and vehicle increase in large cities. Various studies are being conducted to solve these problems. Most of the traffic congestion in cities is caused by traffic signals at intersections. This paper proposes a dynamic traffic light control (DTLC) scheme to support safe vehicle operation and smooth traffic flow using real-time traffic information based on VANET. DTLC receives instantaneous speed and directional information of each vehicle through road side units (RSUs) to obtain the density and average speed of vehicles for each direction. RSUs deliver this information to traffic light controllers (TLCs), which utilize it to dynamically control traffic lights at intersections. To demonstrate the validity of DTLC, simulations were performed on average driving speed and average waiting time using the ns-2 simulator. Simulation results show that DTLC can provide smooth traffic flow by increasing average driving speed at dense intersections and reducing average waiting time.

• Journal of Korean Institute of Industrial Engineers
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• v.16 no.2
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• pp.23-36
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• 1990

A unit-load automated guided vehicle system is considered in which a single vehicle is operated on a unidirectional path in a closed loop. The vehicle serves a manufacturing cell moving pallets from one station to another based on the "First-Encountered-First-Serve" dispatching rule. An approximation method is developed to compute the mean waiting time of an arbitrary pallet at each station. Extensive numerical experiments, performed for various problems, yield fairly good results in most of the cases compared with those obtained by simulation method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.6
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• pp.62-70
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• 2016

Increased bus traffic leads inefficiency at near-side bus stops in median exclusive bus lane because buses are waiting for a signal does not have a vehicle arrived. This study suggests a method for estimating a proper spacing between bus stops and signalized intersection to prevent the inefficiency. We modified the Poisson model for a proper spacing by using both dwell time and waiting time of signal instead of using dwell time only. The waiting time of signal changes by spacing and it was measured using micro simulation program. The iterative algorithm using the change of waiting time of signal was also suggested. By applying the proposed method, measure waiting time by simulation and iterative algorithm, the spacing of near-side bus stops, proper spacing is suggested according to flow rate level.

• Proceedings of the KAIS Fall Conference
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• 2008.05a
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• pp.248-250
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• 2008

This paper proposes a prediction method to prevent traffic accident and reduce to vehicle waiting time using neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dose not consider coordinating green time. Moreover, we present neural network approach for traffic accident prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data. Computer simulation results proved reducing traffic accident waiting time which proposed neural network better than conventional system dosen't consider neural network.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.73-83
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• 2004

With increasing numbers of vehicles on restricted roads, It happens that we have much wasted time and decreased average car speed. This paper proposes a new concept of coordinating green time which controls 10 traffic intersection systems. For instance, if we have a baseball game at 8 pm today, traffic volume toward the baseball game at 8 pm today, franc volume toward the baseball game will be increased 1 hour or 1 hour and 30 minutes before the baseball game. At that time we can not predict optimal green time Even though there have smart electro-sensitive traffic light system. Therefore, in this paper to improve average vehicle speed and reduce average vehicle waiting time, we created optimal green time using fuzzy rules md neural network as a preprocessing. Also, we developed an Intelligent PLC(Programmable Logic Controller) for real time traffic forecasting as a postprocesing about unexpectable conditions. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive franc light system does not consider coordinating green time.