• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.105-111
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• 2003

This paper is researching the storing of 40 different kinds of conditions. Such as, car speed, delay in starting time and the volume of cars in traffic. Through the use of a central nervous networking system or AI, using 10 different intersecting roads. We will improve the green traffic light. And allow more cars to easily flow through the intersections. Now days, with increasing many vehicles on restricted roads, the conventional traffic light creates prove startup-delay time and end-lag-time. The conventional traffic light loses the function of optimal cycle. And so, 30-45% of conventional traffic cycle is not matched to the present traffic cycle. In this paper proposes electro sensitive traffic light using fuzzy look up table method which will reduce the average vehicle waiting time and improve average vehicle speed. Computer simulation results prove that reducing the average vehicle waiting time which proposed considering passing vehicle length for optimal traffic cycle is better than fixed signal method which dosen't consider vehicle length.

• Journal of Korean Society of Transportation
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• v.14 no.1
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• pp.117-133
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• 1996

In the paper a superior performance algorithm compared to the existing vehicle actuated controller and time fixed controller and the additional controller is described through realization of fuzzy traffic phase controller. Fuzzy theory is encouraging since the application is similar to human's decision ability that is approately coped with uncertain conditions. The paper presents that selection of the phase adequated the variable traffic conditions through the fuzzy theory algorithm and decision of optimal cycle time approated the uncertain traffic volume are predominant in traffic jam solution compared to the existing Webster's cycle time decision method and the sequential traffic phase design method and dual-ring phase operation system.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.281-291
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• 2017

Nowadays, as traffic jams are a daily elementary problem in both developed and developing countries, systems to monitor, predict, and detect traffic conditions are playing an important role in research fields. Comparing them, researchers have been trying to solve problems by applying many kinds of technologies, especially roadside sensors, which still have some issues, and for that reason, any one particular method by itself could not generate sufficient traffic prediction results. However, these sensors have some issues that are not useful for research. Therefore, it may not be best to use them as stand-alone methods for a traffic prediction system. On that note, this paper mainly focuses on predicting traffic conditions based on a hybrid prediction approach, which stands on accuracy comparison of three prediction models: multinomial logistic regression, decision trees, and support vector machine (SVM) classifiers. This is aimed at selecting the most suitable approach by means of integrating proficiencies from these approaches. It was also experimentally confirmed, with test cases and simulations that showed the performance of this hybrid method is more effective than individual methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.81-88
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• 2014

These days the researches using IT technologies have been done to decrease the traffic accident. Especially, the optimal safety speed considering the weather conditions have to be calculated in real time to protect the traffic accident on the high way in the case of the rain and snow. In this paper, we have simulated the automatic warning broadcasting system for the freezing and foggy regions based on Web to protect the traffic accident. Also, we have developed the simulator that can provide the drivers with the optimal safety speed in real time to protect the traffic accident even under the worst weather conditions using the Fuzzy Reasoning rules.

• Journal of KIISE:Information Networking
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• v.29 no.1
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• pp.48-56
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• 2002

This paper proposes an IP(Internet Protocol) multicast routing method by multiple tree routes. Multiple trees, instead of a single tree, improve the quality of multicast services with nonlinear link cost and huge traffic demand. The proposed method adds tree routes until it satisfies target conditions, and it splits the multicast traffic demand into the chosen tree routes. We develop a mathematical model and optimal conditions for traffic splitting. The method works on the problems with many different simultaneous multicast traffic. Various experiments were carried and the results show that the new multicasting is fairly effective on end-to-end quality of services.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.101-104
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• 2007

All of noise prediction model have it's own features in the case of modeling conditions, so it is very important to know the features of each model case by case for a proper modeling, especially using at the Environmental Impact Assessment. For prediction of highway traffic noise and abating the noise by barriers, two kinds of prediction model, HW-NOISE, KHTN(Korea Highway Traffic Noise) has been mainly used in Korea. In this study, the features of these models were described at the same conditions. The properties of sound power from a road, diffraction characteristics from a barrier, sound pressure level decaying in each model were investigated. Using the results, it will be anticipated that the proper using of prediction models in the works of highway noise abating.

• Journal of IKEEE
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• v.23 no.4
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• pp.1381-1386
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• 2019

As traffic congestion in cities becomes more serious, intelligent traffic control is actively being researched. Reinforcement learning is the most actively used algorithm for traffic signal control, and recently Deep reinforcement learning has attracted attention of researchers. Extended versions of deep reinforcement learning have been emerged as deep reinforcement learning algorithm showed high performance in various fields. However, most of the existing traffic signal control were studied in a single intersection environment, and there is a limitation that the method at a single intersection does not consider the traffic conditions of the entire city. In this paper, we propose a cooperative traffic control at multi-intersection environment. The traffic signal control algorithm is based on a combination of extended versions of deep reinforcement learning and we considers traffic conditions of adjacent intersections. In the experiment, we compare the proposed algorithm with the existing deep reinforcement learning algorithm, and further demonstrate the high performance of our model with and without cooperative method.

• Journal of the Korean Society of Marine Environment & Safety
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• v.11 no.2 s.23
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• pp.1-8
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• 2005

In the present maritime traffic conditions of Mokpo harbor, there exist many hazardous factors which may lead to huge accidents including marine oil pollution We analyze marine traffic environments including traffic congestion, natural conditions, maritime traffic accidents in the last 10 years, the fishery status, operation of traffic routes and management of navigational aids and regulations relating ships' routeing both in and out of the country. Consequently, this work is to propose improved marine traffic system in future.

• Proceedings of KOSOMES biannual meeting
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• 2005.05a
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• pp.45-52
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• 2005

In the present maritime traffic conditions of mokpo harbor, there exists many hazardous factors which may lead to huge accidents including marine oil pollution We analyze marine traffic environments including traffic congestion, natural conditions, maritime traffic accidents of the last 10 years, the fishery status, operation of traffic routes and navigational aids of navigational aids and regulations relating ships' routeing both in and out of the country. Consequently, this work is to propose improved marine traffic system in future.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1244-1253
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• 2018

The color of traffic light is sensitive to various illumination conditions. Especially it loses the hue information when oversaturation happens on the lighting area. This paper proposes a traffic light recognition method robust to these illumination variations. The method consists of two steps of traffic light detection and recognition. It just uses the intensity and saturation in the first step of traffic light detection. It delays the use of hue information until it reaches to the second step of recognizing the signal of traffic light. We utilized a deep learning technique in the second step. We designed a deep convolutional neural network(DCNN) which is composed of three convolutional networks and two fully connected networks. 12 video clips were used to evaluate the performance of the proposed method. Experimental results show the performance of traffic light detection reporting the precision of 93.9%, the recall of 91.6%, and the recognition accuracy of 89.4%. Considering that the maximum distance between the camera and traffic lights is 70m, the results shows that the proposed method is effective.