• 대한교통학회지
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• 제12권1호
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• pp.97-116
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• 1994

Today advanced traffic management systems are required because of a high increase in traffic demand. Accordingly, the objective of this study is to take advantage of image processing systems and present image processing methods available for collection of the data on traffic characteristics, and then to investigate the possibility of traffic flow analysis by means of comparison and analysis of measured traffic flow. Data were collected at two places of Daegu city and Kyongbu expressway by using VTR. Rear view (down stream) and frontal view (up stream) methods were employed to compare and analyze traffic characteristics including traffic volume, speed, time-headway, time-occupancy, and vehicle-length, by analysis of measured traffic flow and image processing respectively. Judging from the results obtained by this study, image processing techniques are sufficient for the analysis of traffic volume, but a frame grabber equipped with high speed processor is necessary as well, with low level system judged to be sufficient for traffic volume analysis.

• 한국통신학회논문지
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• 제31권12A호
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• pp.1246-1252
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• 2006

본 논문에서는 random FHSS, synchronous FHSS 방식을 채용한 RFID 시스템의 충돌 확률, 트래픽 처리시간 및 주파수 채널 용량 분석을 위한 알고리즘을 제시하였다. 평균 서비스 시간, duty cycle, traffic intensity, 충돌이 발생하여 재전송 시도를 하기 위한 지연시간을 고려하여 정상상태에서의 충돌 확률 및 트래픽 처리 지연시간을 산출하였다. 모의실험을 통하여 최대처리 가능한 트래픽 용량 및 주파수 채널수를 평가하고, 제안된 분석 알고리즘 정확성을 입증하였다.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.951-969
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• 2017

In this paper, we newly propose a traffic information service model that collects traffic information sensed by an individual vehicle in real time by using a smart device, and which enables drivers to share traffic information on all roads in real time using an application installed on a smart device. In particular, when the driver requests traffic information for a specific area, the proposed driver-personalized service model provides him/her with traffic information on the driving directions in advance by predicting the driving directions of the vehicle based on the learning of the driving records of each driver. To do this, we propose a traffic information management model to process and manage in real time a large amount of online-generated traffic information and traffic information requests generated by each vehicle. We also propose a road node-based indexing technique to efficiently store and manage location-based traffic information provided by each vehicle. Finally, we propose a driving learning and prediction model based on the hidden Markov model to predict the driving directions of each driver based on the driver's driving records. We analyze the traffic information processing performance of the proposed model and the accuracy of the driving prediction model using traffic information collected from actual driving vehicles for the entire area of Seoul, as well as driving records and experimental data.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.737-740
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• 1999

This paper deals with a real time optimization algorithm within real time for DRGS(Dynamic Route Guidance System) and evaluate the algorithm. A pre-developed system offers the optimal route in using only static traffic information. In using real-time traffic information, Dynamic route guidance algorithm is needed. The serious problem in implementing it is processing time increase as nodes increase and then the real time processing is impossible. Thus, in this paper we propose the optimal route algorithm with window mechanism for the real-time processing and then evaluate the algorithms.

• 한국통신학회논문지
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• 제24권12A호
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• pp.1910-1916
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• 1999

본 논문은 버스트 트래픽 특성을 갖는 ATM 망에서 입력 버퍼 제어형 ATM 스위치의 서비스 률 및 성능 문제점을 해결하기 위해 임의의 시간 간격을 갖고 입력되는 입력 트래픽의 전단에, 셀 다중화기를 두어 셀 들을 다중 분리하고 각 셀 들의 도착 시간 간격과 처리 요구 시간(due time)을“표기(marking)”하여 각각의 입력 버퍼로 전송하고, 서버는 각 입력 버퍼의 헤더에 있는 셀 도착 시간 간격과 셀 들의 처리 요구 시간을 비교하여 처리 요구 시간이 가장 작은 값을 가지는 셀을 우선 처리하여 전송토록 하였다. 이렇게 함으로써 실 시간적으로 빠르게 전송되어야 하는 셀 들을 비 실 시간적으로 전송되어도 되는 셀 보다 먼저 전송하여 셀 처리 시간을 보장하고, 지연에 민감한 트래픽을 우선 처리함으로서 CBR 및 VBR의 트래픽 특성을 만족토록 하였다. 이러한 셀 처리 요구 시간 및 우선 순위를 고려한 ATM 스위치의 성능을 분석하기 위해서 각 출력 포트당 4개의 가상 버퍼를 설정하고 각각의 버퍼에 ATM Forum에서 권고하는 CBR/rt-VBR(셀 손실 및 지여 우선순위), nrt-VBR(셀 손실 우선순위), ABR(셀 지연 우선순위) 및 UBR(하위순위) 트래픽 특성에 따?, 셀의 도착 시간과 셀 처리 요구 시간을 다르게 부과하여 최적의 서비스 파라메타 값을 도출하고 각각의 트래픽 특성에 따른 서비스를 분석하였다.

• Journal of Information Processing Systems
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• 제7권1호
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• pp.1-16
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• 2011

With the increasing number of vehicles in use in our daily life and the rise of traffic congestion problems, many methods and models have been developed for real time optimisation of traffic lights. Nevertheless, most methods which consider real time physical queue sizes of vehicles waiting for green lights overestimate the optimal cycle length for such real traffic control. This paper deals with the development of a generic hybrid model describing both physical traffic flows and control of signalised intersections. The firing times assigned to the transitions of the control part are considered dynamic and are calculated by a simplified optimisation method. This method is based on splitting green times proportionally to the predicted queue sizes through input links for each new cycle time. The proposed model can be easily translated into a control code for implementation in a real time control system.

• 한국정보전자통신기술학회논문지
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• 제9권1호
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• pp.100-108
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• 2016

본 논문에서는 교차로에 설치된 카메라를 이용하여 각 도로로 유입 유출되는 교통량을 동시에 측정할 수 있도록 하였으며, 측정한 데이터를 퍼지논리에 적용하여 녹색 신호를 제어하는 시스템을 제안한다. 기존의 퍼지논리를 이용한 신호등 제어 시스템은 신호대기 중인 차량 숫자를 측정하여 기반 데이터로 사용하였으나, 본 논문에서는 영상처리를 이용하여 측정한 교차로 유입 차량 수를 퍼지논리의 기반 데이터로 사용하여 심각한 교통 정체가 일어나기 전에 이를 미연에 방지 할 수 있는 신호 제어로직을 고안한다. 본 논문에서 제안하는 교통신호 자동 제어로직을 활용하여 교통정체가 일어나기 전에 각 도로간 교통량을 조절함으로써 교통 정체로 발생하는 운전자의 시간 낭비 및 에너지 낭비를 예방한다.

• 한국정보처리학회논문지
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• 제7권10호
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• pp.3195-3200
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• 2000

본 논문은 ATM망의 통화유랑 제어중 최적한 폭주제어의 실현을 위해 필수적인 다중매체 통화유량 예측에 관한 논문으로서 ATM망에 유입 될 다중매체 통화유량의 특성이 시대의 발전에 따라 서서히 변화될 것이 예상되므로 모의실험에 사용 될 다중매체 통화유랑을 단위시간당 접속호수는 프아송분포, 각 호당 요구전송속도는 감마분포, 각 호의 유지시간은 지수분포를 기준으로 하여 각각의 분포특성을 변화시켜 통화유량 특성변화를 유도하여 발생시킨 후 이를 신경망과 실시간 처리를 위해 제안된 3중신경망 모델[3]로 추정하여 비교함으로써 제안된 모델이 ATM망의 통화유량 예측에 이용될 수 있음을 보인다.

• 제어로봇시스템학회논문지
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• 제9권7호
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• pp.498-506
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• 2003

A computer vision system applied to an intelligent safety vehicle has been required to be worked on a small sized real time special purposed hardware not on a general purposed computer. In addition, the system should have a high reliability even under the adverse road traffic environment. This paper presents a design and an implementation of an onboard hardware system taking into account for high speed image processing to analyze a road traffic scene. The system is mainly composed of two parts: an early processing module of FPGA and a postprocessing module of DSP. The early processing module is designed to extract several image primitives such as the intensity of a gray level image and edge attributes in a real-time Especially, the module is optimized for the Sobel edge operation. The postprocessing module of DSP utilizes the image features from the early processing module for making image understanding or image analysis of a road traffic scene. The performance of the proposed system is evaluated by an experiment of a lane-related information extraction. The experiment shows the successful results of image processing speed of twenty-five frames of 320$\times$240 pixels per second.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 추계학술발표대회
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• pp.362-364
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• 2018

Predicting network traffic volume has become a popular topic recently due to its support in many situations such as detecting abnormal network activities and provisioning network services. Especially, predicting the volume of the next upcoming traffic from the series of observed recent traffic volume is an interesting and challenging problem. In past, various techniques are researched by using time series forecasting methods such as moving averaging and exponential smoothing. In this paper, we propose a long short-term memory neural network (LSTM) based network traffic volume prediction method. The proposed method employs the changing rate of observed traffic volume, the corresponding time window index, and a seasonality factor indicating the changing trend as input features, and predicts the upcoming network traffic. The experiment results with real datasets proves that our proposed method works better than other time series forecasting methods in predicting upcoming network traffic.