• 한국산학기술학회논문지
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• 제18권12호
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• pp.93-99
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• 2017

지난해 국내에서 발생한 교통사고 사망자 수는 OECD 회원국 가운데 인구 10만명당 10명으로 35개국 중 6위를 기록하고 있고, 어린이나 노인과 같은 교통약자의 사고율도 높은 수준에 있다. 본 연구에서는 관련 문헌 검토, 교통사고분석시스템 자료를 이용한 사고요인분석 및 교통사고 특성 분석 등을 통하여 국내 비도심 지역 교통약자의 교통사고 저감을 위해 개발하고 있는 교통안전시설물을 소개하고자 한다. ICT기반 횡단보도용 교통안전 통합시설물은 어린이보호구역의 횡단보도를 우선 검토대상으로 하여 불법주차 차량을 배제하며, 보행자에게 횡단보도에 접근 차량이 있음을 알려주는 스마트 안전 휀스와 횡단보도 보행자가 있음을 인지하지 못한 운전자에게 경고하는 스마트 방지턱으로 구성하여 상호 작동하도록 설계하였다. 횡단보도용 교통안전시설물의 적정 형태 및 규모를 표준화하기 위하여 도로 기능, 보도 구분, 전력, 차로 수, 기학적 형태 등을 고려한 타입별 표준모델을 구축하였고, 시설물의 요구 기능을 정의하여 아이디어를 구체화하였다. 이에 따라, 교통약자의 교통사고를 저감하고, 태양광 전력공급, 기존 설치된 안전 휀스와의 호환성을 염두에 둔 디자인으로 유지관리비용 절감효과를 얻을 수 있을 것으로 기대한다.

• 한국BIM학회 논문집
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• 제12권2호
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• pp.1-11
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• 2022

Road traffic accidents (RTAs) are the leading cause of accidental death among children. RTA reduction is becoming an increasingly important social issue among children. Municipalities aim to resolve this issue by introducing "Smart Pedestrian Crosswalks" that help prevent traffic accidents near children's facilities. Nonetheless such facilities tend to be installed in relatively limited number of areas, such as the school zone. In order for budget allocation to be efficient and policy effects maximized, optimal location selection based on machine learning is needed. In this paper, we employ machine learning models to select the optimal locations for smart pedestrian crosswalks to reduce the RTAs of children. This study develops an optimal location index using variable importance measures. By using k-means clustering method, the authors classified the crosswalks into three types after the optimal location selection. This study has broadened the scope of research in relation to smart crosswalks and traffic safety. Also, the study serves as a unique contribution by integrating policy design decisions based on public and open data.

• 대한토목학회논문집
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• 제34권1호
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• pp.243-250
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• 2014

기존의 신호교차로에 비해 상대적으로 효율적이고 안전한 것으로 평가되고 있는 회전교차로(roundabout)가 최근 전국적으로 설치 운영 중에 있으며 이에 따라 회전교차로의 효율성과 안전성을 개선하기 위한 다양한 연구가 수행 중에 있다. 본 연구에서는 현재까지 시도된 적이 없는 회전교차로와 커넥티드 차량(connected vehicle) 기법의 접목을 통해 Smart Roundabout이라는 새로운 개념의 첨단교통정보시스템을 개발하고 미시적 시뮬레이션을 이용하여 평가하고자 한다. Smart Roundabout은 교차로를 회전하는 차량에 장착된 단말기(on-board equipment, OBE)를 통해 차량들의 주행정보(위치, 속도, 차두시간 등)를 커넥티드 차량(connected vehicle) 기법을 통해 전달받고 회전교차로에 접근하는 차량 내부의 단말기를 통해 교차로 진입에 대한 상환판단을 도와 안전성을 확보함과 동시에 보다 짧은 차두시간(critical headway)을 구현하여 회전교차로의 용량을 증대시킬 수 있을 것으로 기대된다.

• 대한안전경영과학회지
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• 제24권2호
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• pp.113-126
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• 2022

This study examined the effect of railway traffic controllers' negative perception of organizational culture on their job demand and job satisfaction in relation to the moderating effect of self-efficacy. Results showed that the aggressive/defensive culture based on power and competition, had a positive (+) effect on job demand and job satisfaction. On the other hand, in the conditional process model in which self-efficacy affects the relationship between organizational culture, job demand, and job satisfaction, self-efficacy played a significant role in lowering the level of job demand, and it contributed to the increase of job satisfaction through a mediating effect. This results suggest the needs for lowering the level of job demand by changing the present aggressive/defensive organizational culture into the constructive one. And also, much consideration for maintaining the level of their self-efficacy should be spent.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.162-164
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• 2022

Efforts are being made to prevent traffic accidents within the child protection zone. Efforts are being made to prevent accidents by enacting safety facilities and laws to prevent traffic accidents in the school zone. However, traffic accidents in school zones continue to occur. If the driver can know the situation in the child protection zone in advance, accidents can be reduced. In this paper, we design a camera that eliminates blind spots in school zones and a number recognition camera system that can collect pre-traffic information. Design a LIDAR system that recognizes vehicle speed and pedestrians. Design an LED guidance system that delivers information to drivers without smart devices. We study time series analysis and artificial intelligence algorithms that collect and process pedestrian and vehicle information recognized by cameras and LIDAR. In the artificial intelligence traffic accident prevention system learned by deep learning, before entering the school zone, the school zone information is sent to the driver through the Force Push Service and the school zone information is delivered to the driver on the LED sign. try to reduce accidents.

• 융합신호처리학회논문지
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• 제13권2호
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• pp.106-112
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• 2012

스마트하이웨이는 고속 주행하는 운전자에게 교통안전 개선 및 교통사고 발생률 감소, 지능적이고 편리한 주행환경을 지원하는 지능형 고속도로이다. 스마트하이웨이를 구현하기 위해 장애물, 야생동물, 고장차등과 같은 위험상황 정보 수집이 필요하다. 현재 고속도로에서 다양한 센서를 이용하여 교통정보를 수집하고, 분석하여 운전자에게 제공하고 있다. 그러나 이와 같은 기법은 다양한 정보수집의 한계, 기상여건에 따른 정확도의 결여 및 유지관리의 한계 등의 문제가 있다. 따라서 위험 정보를 수집하여 운전자에게 안전주행정보를 제공하기 위해서는 레이더 시스템이 필요하다. 본 논문에서는 위험 정보를 수집하기 위하여 개발한 34.5GHz RWR(Road Watch Radar) 레이더를 사용하였고, 현장 시험을 통해 레이더의 장애물 탐지 성능, 분해능 성능을 입증하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1694-1699
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• 2003

A windshield wiper system plays a key part in assuring the driver's safety during the rainfall. However, because the quantity of rain and snow vary irregularly according to time and the velocity of the automobile, a driver changes wiper speed and interval from time to time to secure enough visual field in the traditional windshield wiper system. Because a manual operation of windshield wiper distracts driver's sensitivity and causes inadvertent driving, this is becoming a direct cause of traffic accidents. Therefore, this paper presents the basic architecture of a vision-based smart windshield wiper system and a rain sensing algorithm that regulates speed and interval of the windshield wiper automatically according to the quantity of rain or snow. This paper also introduces a fuzzy wiper control algorithm based on human's expertise, and evaluates the performance of the suggested algorithm in an experimental simulator.

• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.145-151
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• 2018

While commercialization of IoT technologies in the safety management sector is being promoted in terms of industrial safety of large indoor businesses, implementing a system for risk management of small outdoor work sites with frequent site movements is not actively implemented. In this paper, we propose an efficient dynamic workload balancing strategy which combined low-power, wide-bandwidth (LPWA) communication and low-power Bluetooth (BLE) communication technologies to support customized risk management alarm systems for each individual (driver/operator/manager). This study was designed to enable long-term low-power collection and transmission of traffic information in outdoor environment, as well as to implement an integrated real-time safety management system that notifies a whole field worker who does not carry a separate smart device in advance. Performance assessments of the system, including risk alerts to drivers and workers via Bluetooth communication, the speed at which critical text messages are received, and the operation of warning/lighting lamps are all well suited to field application.

• 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.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.1-7
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• 2020

This study is a study on the development of AEBS test scenarios for traffic accidents in Korea, and was compared and analyzed using the Traffic Accident Analysis Program. To ensure the safety of passengers and pedestrians in traffic accidents, the number of cars equipped with ADAS is increasing rapidly at all car manufacturers in each country. For traffic accidents used in this study, the domestic traffic accident database (ACCC) produced by SAMSONG was used. Domestic traffic accidents differ from overseas traffic accidents in terms of road type, signal system, driver's seat location and number of vehicles. ACCC databases, which supplemented and reinforced these differences, built a database based on the PC-CRASH program. In the study, we analyze the types of accidents to develop comparative scenarios for each type of road and collision type of traffic accidents. When the road types of traffic accidents in Korea were divided into five types and the collision types were divided into six, it was confirmed that the most types of FRONT-SIDE crashes appeared at the intersection. It is expected that the frequency of possible traffic accidents and collision types can be predicted according to the road type in the accident database, we that it can be used as an AEBS test scenario development suitable for the domestic road environment.