• Journal of Korean Society of Transportation
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• v.21 no.4
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• pp.79-90
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• 2003

Internal metering policy(IMP) is a control strategy to improve the quality of traffic flow within a network by avoiding queue spillback or intersection blockage. It is a more aggressive control strategy than the current control strategy employed in COSMOS. A preliminary study was made to incorporate the IMP logic within the COSMOS system to improve its' functionality at oversaturated conditions. From the study results, a set of guideline for real implementation was recommended : traffic conditions, detector configurations, and control procedures, etc. A simulation study was performed to evaluate the effectiveness of the proposed guidelines. It was shown that the occurrence of queue spillback was minimized. and overall network performance was also improved by applying IMP logic as compared to COSMOS control onl.

• Journal of Korean Society of Transportation
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• v.15 no.2
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• pp.67-82
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• 1997

일반적으로 교통수요가 용량보다 적으면 모든 교통량이 지체없이 신호교차로를 통 과 할 수 있을 것이다. 이러한 비포화 상태에서는 어떻게 Delay나 Stop을 최소화시키느냐가 신호처리의 목적함수가 될 것이다. 그러나 교통수요가 용량보다 많아지면 신호교차로가 모 든 교통량을 통과시키지 못하므로 시간이 갈수록 대기 행렬이 점점 길어질 것이다. 이러한 과포화상태에서는 늘어나는 대기 행렬을 조절하지 못하면 결국에는 Spillback이 상류 교차 로로 확대되어 최악에는 교차로에서의 모든 방향의 움직임을 정지시키는 Gridlock상태로까 지 악화 될 수 있다. 따라서 과포화 상태에서는 비포화 상태와는 달리 늘어나는 대기행렬을 조절하여 통과 교통량을 최대화 시키는 것이 신호처리의 목적 함수가 될 수 있을 것이다. 본 논문에서는 과포화시의 간선도로를 신호처리에 의해 일정한 대기행렬을 유지하므로써 시 스템을 최적화 하는 알고리즘을 개발하였다. IMPOST(Internal Metering Policy to Optimize Signal Timing)는 논문에서 개발한 알고리즘을 C언어로 프로그래밍한 model이다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.360-366
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• 2005

In the past, when there were few vehicles on the road, the T.O.D.(Time of Day) traffic signal worked very well. The T.O.D. signal operates on a preset signal cycling which cycles on the basis of the average number of average passenger cars in the memory device of an electric signal unit. Now days, with increasing many vehicles on restricted roads, the conventional traffic light creates 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 we 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 doesn't consider vehicle length.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.66-71
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• 2003

In the past, when there were few vehicles on the road, the T.O.D.(Time of Day) traffic signal worked very well. The T.O.D. signal operates on a preset signal cycling which cycles on the basis of the average number of average passenger cars in the memory device of an electric signal unit. Today, with increasing traffic and congested roads, the conventional traffic light creates startup-delay time and end lag time so that thirty to forty-five percent efficiency in traffic handling is lost, as well as adding to fuel costs. To solve this problem, this paper proposes a new concept of optimal green time algorithm, which reduces average vehicle waiting time while improving average vehicle speed using fuzzy rules and neural networks. Through computer simulation, this method has been proven to be much more efficient than fixed time interval signals. Fuzzy Neural Network will consistanly improve average waiting time, vehicle speed, and fuel consumption.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.76-84
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• 2003

Now days, It is based on GIS and GPS, it can search for the shortest path and estimation of arrival time by using the internet and cell phone to driver. But, even though good car navigation system does not create which is the shortest path when there average vehicle speed is 10 -20 Km. Therefore In order to reduce vehicle waiting time and average vehicle speed, we suggest optimal green time algorithm using fuzzy adaptive control, where there are different traffic intersection length and lane. In this paper, it will be able to forecast the optimal traffic information, estimation of destination arrival time, under construction road, and dangerous road using internet.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.722-728
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• 2003

In this paper, we propose a fuzzy traffic controller of Sugeno`s fuzzy model so as to model the nonlinear characteristics of controlling the traffic light. It use a degree of the traffic congestion of the preceding roads as an input so that it can cope with traffic congestion appropriately, which causes the loss of fuel and our discomfort. First, in order to construct fuzzy traffic controller of Sugeno`s fuzzy model, we model the control process of the traffic light by using Mamdani`s fuzzy model, which has the uniform membership functions of the same size and shape. Second, we make Mamdani`s fuzzy model with the non-uniform membership functions so that it can exactly reflect the knowledge of experts and operators. Last, we construct the fuzzy traffic controller of Sugeno`s fuzzy model by learning from the input/output data, which is retrieved from Mamdani`s fuzzy model with the non-uniform membership functions. We compared and analyzed the fixed traffic light controller, the fuzzy traffic controller of Mamdani`s fuzzy model and the fuzzy traffic controller of Sugeno`s fuzzy model by using the delay time and the proportion of the entered vehicles to the occurred vehicles. As a result of comparison, the fuzzy traffic controller of Sugeno`s fuzzy model showed the best performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.51-62
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• 1996

Electro sensitive traffic system can not consider passenger car unit, so it causes start up delay time and passenger waiting time. In this paper, it antecedently creates passenger car unit at the bottom intersection using neural network. But, sometimes it can make mistakes due to changes in car weight, car speed, and passing area. Therefore, it consequently reduces the car waiting time and start-up delay time using fuzzy control of feed-back data. Moreover, to prevent spillback, it can adapt control even though upper traffic intersection has a different saturation rate, road length, road slope and road width.