• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.1
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• pp.100-108
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• 2016

In this paper, We propose a method which prevents severe traffic jam by controlling traffic signal by itself based on image-processed information and fuzzy logic. The detailed idea of this method is first to let a closed monitoring camera gather the number of cars which show the flow of traffic the designated roads which are commonly considered to have traffic. After executing the image processing method on each image gathered from the monitoring camera, this system determines the changing timing of traffic signal based on fuzzy logic. Also, this image processing method shows good performance in real road environment because the setup background image which used in this system is designed to be updated in real time. All of good points mentioned above would lead driver and users to cost efficient and time efficient results by preventing the increase of the number of traffic on road in advance with the automatic traffic signal controlling algorithm based on the fuzzy logic.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.161-170
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• 2001

Vehicle detection is the basic of traffic monitoring. Video based systems have several apparent advantages compared with other kinds of systems. However, In video based systems, shadows make troubles for vehicle detection. especially active shadows resulted from moving vehicles. In this paper a new method that combines background subtraction and edge detection is proposed for vehicle detection and shadow rejection. The method is effective and the correct rate of vehicle detection is higher than 98(%) in experiments, during which the passive shadows resulted from roadside buildings grew considerably. Based on the proposed vehicle detection method, vehicle tracking, counting, classification and speed estimation are achieved so that traffic information concerning traffic flow is obtained to describe the load of each lane.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.173-177
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• 2016

Korea, the traffic and transportation problems are significant because private cars are increasing constantly. Therefore, it is imperative to improve traffic condition so as to solve the problems such as traffic congestion and accidents which may occur due to the increase of vehicles in a limited area through the signal control. However, the current operating system for traffic control cannot provide car users the optimal signal but it generates a time delay of vehicles, traffic congestions etc. In this paper, we propose and implement the system based on V2X based automatic controller, which reduces the waste of time and the driver's psychological stress on the road intersection.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.3
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• pp.38-47
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• 2008

A vehicle counting system at intersection is designed and implemented using analyzing a video stream from a camera. To separate foreground image from background, we compare three different methods, among which Li's method is chosen. Blobs are extracted from the foreground image using connected component analysis and the blobs are tracked by a blob tracker, frame by frame. The primary tracker use only the size and location of blob in foreground image. If there is a collision between blobs, the mean-shift tracking algorithm based on color distribution of blob is used. The proposed system is tested using real video data at intersection. If some huristics is applied, the system shows a good detection rate and a low error rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.8
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• pp.1885-1894
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• 2014

There are various high-performance algorithms in the area of the existing VDSs (vehicle detection systems). However, they requires a large amount of computational time-complexity and their systems generally are very expensive and consumes high-power. This paper proposes real-time traffic information detection algorithm that can be applied to low-cost, low-power, and open development platform such as Android. This algorithm uses a vehicle's sub-shadow to set ROI(region of interest) and to count vehicles using a location of the sub-shadow and the vehicle. The proposed algorithm is able to count the vehicles per each roads and each directions separately. The experiment result show that the detection rate for going-up vehicles is 94.1% and that for going-down vehicles is 97.1%. These results are close to or surpasses 95%, the detection rate of commercial loop detectors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.2
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• pp.57-66
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• 2012

This study intended to evaluate the operational performance change from the introduction of the bus signal priority system using the field data. To complete the objective, travel time and volume data were collected from the before and after study, then the distribution of individual vehicle's travel time and the difference of travel time and traffic volume were compared respectively. Analysis results showed that no significant volume change was observed from both passenger vehicle and bus for the major and cross streets. It was identified that the quality of travel time distributions of passenger vehicle and bus was improved after introducing the bus signal priority system. In terms of average speed, passenger car in a major direction increased by 6.5% and bus increased by 10.5% in general. Statistical tests showed that those speed differences were statistically significant at the 95% of confidence level. The results of this paper will be a good source for further research in the area of bus signal priority control.

• 한국ITS학회:학술대회논문집
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• 2002.11a
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• pp.169-172
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• 2002

차량검출에 관한 연구는 교통량 관측을 위해서 가장 기본적이고 필수적인 요소이다. 영상을 기반으로 한 교통 정보 시스템은 다른 방식을 이용하는 시스템들과 비교했을 때 여러가지 두드러진 장점을 가지고 있다. 하지만 일반적인 영상기반 시스템에서는 기상상태에 관해서 민감하게 반응하지 못하는 단점이 있다. 악천후가 발생하는 환경에서 영상의 노이즈는 차량의 교통정보 추출에 있어서 심각한 성능의 저하를 야기할 수 있다. 본 논문에서는 차량검출과 함께 기상 상태에 대해 영향을 덜 받는 향상된 차량정보 추출 방식을 제안 하였다. 제안된 방법은 에지를 기반으로 추출된 차량영상으로부터 비나 눈으로 인한 악천후 때문에 생긴 영상 잡음을 제거 하는 방식으로 기존의 방식에 비해 차량검출 정확도의 오류가 감소되었다. 본 논문에서 제안한 robust 한 차량검출 방법을 기반으로 하여 차량추적, 차량계수, 차종분류, 그리고 속도측정을 수행하여 각 도로의 부하르 나타내는 데 사용되는 차량 흐름과 관련되 여러 가지 교통 정보들을 추출하는데 응용될 수 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.448-451
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• 2005

For measuring a traffic symbolic confusion quantity and symbolic air pleasantness, we use fuzzy sensor algorithm maded by symbolic information quantity. But for implementation of fuzzy sensor, we use some symbolic information item, this method cannot produce precise output because we use vague fuzzy rule method and we cannot abundance fuzzy for precision of fuzzy rule method. For this reason this paper introduce new fuzzy sensor algorithm composed of not fuzzy rule method but using Analytic Hierachy Process. To prove that new method is good, two type of fuzzy sensor applied to traffic signal controller and through much passing vehicle, two fuzzy sensor compared each other.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.193-201
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• 2002

For measuring a traffic symbolic confusion Quantity and symbolic air pleasantness, we use fuzzy sensor algorithm maded by symbolic information Quantity. Hut for implementation of fuzzy sensor, we use some symbolic information item, this method cannot produce precise output because we use vague fuzzy rule method and we cannot abundance fuzzy for precision of fuzzy rule method. For this reason, this paper introduce new fuzzy sensor algorithm composed of not fuzzy rule method but using Analytic Hierachy Process. To prove that new method is good, two type of fuzzy sensor applied to traffic signal controller and through much passing vehicle, two fuzzy sensor compared each other.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.78-96
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• 2023

Urban traffic information is collected by various systems such as VDS, DSRC, and radar. Recently, with the development of deep learning technology, smart intersection systems are expanding, are more widely distributed, and it is possible to collect a variety of information such as traffic volume, and vehicle type and speed. However, as a result of reviewing related literature, the performance evaluation criteria so far are rbs-based evaluation systems that do not consider the deep learning area, and only consider the percent error of 'reference value-measured value'. Therefore, a new performance evaluation method is needed. Therefore, in this study, individual error, interval error, and overall error are calculated by using a formula that considers deep learning performance indicators such as precision and recall based on data ratio and weight. As a result, error rates for measurement value 1 were 3.99 and 3.54, and rates for measurement value 2 were 5.34 and 5.07.