• 전자공학회논문지B
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• 제30B권6호
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• pp.100-108
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• 1993

In this paper, noticing the point of human's ability which appropriately cope with vague conditions, we design fuzzy traffic signal light controller similar to human's distinction ability and decide the optimum cycle most suited to any traffic junction using fuzzy identification algorithm. In this study, for the control output decision process we design fuzzy controller better than electronic vehicle actuated controller in performance. We propose the cycle decision method which is not limited by the variance of traffic junction vehicle number through overcoming the limit of Webster's method which is adopted by the fixed cycle controller. Simulated experimental results show that fuzzy controller and fuzzy identification algorithm are better than the existing electronic vehicle actuated controller and fixed cycle controller in delay time per vehicle.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 춘계학술대회 학술발표 논문집 제15권 제1호
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• pp.161-165
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• 2005

최적 교통 주기를 산출하기 위해서는 하위교차로에 대기차량이 얼마나 있는지를 점검해야 한다. 왜냐하면 대기차량이 상위교차로의 길이보다 크면 출발 지연 시간 및 승용차 대기시간이 발생하기 때문이다. 승용차 대기시간을 단축시키기 위해서 본 논문에서는 퍼지 신경망을 이용한 최적 연동 녹색시간 알고리즘을 제안한다. 컴퓨터 모의실험을 통해서, 서로 다른 교차로 조건을 고려하지 않은 고정 교통신호등 보다 평균 주행속도가 향상 된 것을 입증하였다.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.85-90
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• 2023

The Ant Colony System (ACS) is a variant of Ant colony optimization algorithm which is well-known in Traveling Salesman Problem. This paper proposed a hybrid method based on genetic algorithm (GA) and ant colony system (ACS), called GACS, to solve traffic routing problem. In the GACS, we use genetic algorithm to optimize the ACS parameters that aims to attain the shortest trips and time through new functions to help the ants to update global and local pheromones. Our experiments are performed by the GACS framework which is developed from VANETsim with the ability of real map loading from open street map project, and updating traffic light in real-time. The obtained results show that our framework acquired higher performance than A-Star and classical ACS algorithms in terms of length of the best global tour and the time for trip.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.610-630
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• 2020

A large number of people suffered from traffic accidents each year, so people pay more attention to traffic safety. However, the traditional methods use laser sensors to calculate the vehicle distance at a very high cost. In this paper, we propose a method based on deep learning to calculate the vehicle distance with a monocular camera. Our method is inexpensive and quite convenient to deploy on the mobile platforms. This paper makes two contributions. First, based on Light-Head RCNN, we propose a new vehicle detection framework called Light-Car Detection which can be used on the mobile platforms. Second, the planar homography of projective geometry is used to calculate the distance between the camera and the vehicles ahead. The results show that our detection system achieves 13FPS detection speed and 60.0% mAP on the Adreno 530 GPU of Samsung Galaxy S7, while only requires 7.1MB of storage space. Compared with the methods existed, the proposed method achieves a better performance.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2010년도 추계 학술발표회
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• pp.1302-1313
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• 2010

The west express way is notorious for extremely heavy traffic area in the west of Seoul, South Korea. Hence, the city government recently initiated a new underground BTO (Build-Transfer-Operate) road project to solve traffic congestion and a high construction cost. The proposed underground express road being designed is the first double-level tunnel ever designed in South Korea and using Conventional tunnelling method (NATM). A total length of tunnel for light vehicles is 10.91km long including both open cut structures and concrete lining with middle-deck in bored tunnel. There are also 4 ventilation shafts for ventilation and evacuation on emergency. Many design issues had been aroused during the preliminary design phase and detail design phase is currently going on. This paper discusses design focuses including excavation methods, ground water issues, a deck slab installation, and a construction cost etc. for the double level road tunnel design of urban area.

• 대한가정학회지
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• 제44권2호
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• pp.93-101
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• 2006

Children's wear needs extra safety features to avoid unexpected dangers. On the way to school, children are exposed to traffic very often. Especially in the early morning or late evening, or on dark cloudy days, they might be unrecognized by drivers without safety reflectors on their clothing as a means of raising visibility. Therefore, safety reflectors on clothing can protect children from traffic accidents. The research was carried out as follows. 1. Reflector manufacturers were interviewed regarding how Reflective and Reflexite reflect light back to the light source, what kind of safety reflector products are available, and how these materials are being used in the clothing industry. 2. Mothers of primary school children were interviewed to find out what they think about the effect of safety reflectors, the need for clothing using safety reflectors and the design preferences. 3. In order to apply safety reflectors efficiently, the position on the clothing of the greatest visibility from the light source must be determined. 4. Shirts, pants and jumpers for 8-year-old girls and boys were designed as a case study of applying safety reflectors to clothing. The designs were verified through wearing test.

• 한국정보전자통신기술학회논문지
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• 제13권1호
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• pp.88-93
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• 2020

최근 SDN 기술이 실제 통신 사업에 적용되면서 사용자가 많아지며 네트워크에 흐르는 데이터 량이 많아짐에 따라 네트워크 데이터 흐름 관리에 대한 관심이 늘고 있다. 이 과정에서 전송되는 네트워크 상의 데이터의 기밀성, 무결성, 가용성, 추적 가능성이 보장되는지 확인할 수 있어야 한다. 또한, 다양한 분야에서 요구되는 네트워크상에서 데이터를 실시간으로 흐름을 관측하고 통제를 시각적으로 확인할 수 있는 환경이 개발이 필요하다. 본 논문에서는 첫 번째로 Mininet을 응용하여 네트워크 토폴로지를 시각적으로 구성하고 다양한 속성을 부여할 수 있는 환경을 구축하였다. 둘째, Mininet 환경에서 OpenDayLight를 추가하여 네트워크 토폴로지에서 네트워크 트래픽 흐름을 시각적으로 확인하고 제어할 수 있는 시뮬레이션 환경을 개발하였다.

• 한국산학기술학회논문지
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• 제19권1호
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• pp.577-586
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• 2018

본 연구에서는 국내 지역 비도심 지역의 교통사고 저감을 위해 충청권을 대상으로 관련 문헌 검토와 TAAS(Traffic Accident Analysis System)자료를 활용하여 비도심 지역의 인구특성 및 사고유형을 통하여 지역특성에 맞는 교통안전기술을 개발하였다. 비도심 지역의 교통인구의 보행자 특성에 맞게 개발된 노견용 인식등은 지역특성사 인도가 아닌 길가장자리로 통행이 많은 지역주민들의 교통안전 향상을 위한 교통안전시설물로써 야간 또는 새벽에 통행시 운전자에게 보행자의 시인성을 향상시켜주며 교통안전시설물이며, 보행자 건널목 인식등은 야간 횡단보도 횡단시 운전자에게 횡단중이란 앞서 인지시켜 주어 횡단보도 통해시 보행자의 안정감을 향상시키는 교통안전시설물이다 또한, 기존 시설물인 신호등 및 가로등과 같은 빛 공해에 의한 농작물 피해를 최소화하며, 차량, 농기계와 같은 충돌파손의 문제점을 보완하였다. 기존 시설물인 가로등과 신호등의 사례와 타기술을 비교하여 절감비용을 공사비 및 유지관리비용으로 분석하여, 본 연구에서 개발한 기술의 비용적인 측면에서 효율성을 입증하였다. 현재 본 연구에서 개발한 교통 안전시설물의 적용성과 효과성을 위해 비도심 지역을 기반으로 테스트베드를 구축하여 현재 평가 중에 있으며, 향후 연구를 통해 지속적인 자료의 수집과 분석을 통해 개발된 경제성 분석 및 환경 영향에 맞춤형 적정기술로 개선 될 수 있을 것으로 기대되며, 본 연구에서 개발된 교통안전시설물을 통해 우리나라 교통안전수준의 향상과 비도심 지역의 교통안전이 향상되기를 기대한다.

• 대한원격탐사학회지
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• 제35권1호
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.