• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.5
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• pp.542-549
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• 2019

Maritime accidents, such as the collision of a fishing boat in Incheon's Yeongheung Island, result in several casualties and property damage, even if they occur just once. To prevent such accidents, the Incheon Metropolitan Government is implementing safety management policies; further, they will provide ship collision warning services to prevent collisions on WAVE (Wireless Access in Vehicular Environment) communication-based ship safety operation pilot projects. However, to realize these objectives, a service standardization method is required that defines specific service types, configurations, and systems, which should be prepared based on user requirement analyses. In this study, a standardization method of WAVE communication-based collision warning service was developed by analyzing the requirements of the vessel operators subject to the services and related authorities. This will help improve the quality of service, ensuring professionalism and reliability through continuous improvement and efforts for standardization, as well as data derived from demonstration projects. Therefore, it is expected to help prevent maritime accidents to a considerable extent.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• International Journal of Highway Engineering
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• v.11 no.1
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• pp.105-115
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• 2009

According to the accident statistics published by the National Police Agency in 2007, the number of commercial vehicle(city, suburb and other buses) accidents consumes 3.5 percent of the total number of traffic accidents in this year. Since the commercial vehicles are responsible for not only the drivers but also the passengers, it leads more serious social and economic problems. There have been various forms of systems such as a digital speedometer or a black box to meet the social requirement for reducing traffic accidents and safe driving. however the system based on the data after accident control the driver by analyze dangerous drive behaviors, so there is a limit to control driver in real-time. Also speedometer currently managed provide the driver warning information in real-time, but using only the speed of vehicle and RPM information regardless of actual dangerous drive behaviors, disappear the effectiveness. In this study performed a simulation for drivers in general using a simulator programed with dangerous driving types we had developed in the previous study and judging the types. It'd be more effective system to provide the drivers warning information using weight valued in this study. However in this study is limited to apply weight as a result of simulation of drivers in general in actual situation should be made up the deficit based on information of driving type of actual commercial vehicles.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2010.10a
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• pp.10-12
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• 2010

환경 민감 지도는 해양 유출유 사고 시 효율적이고 신속한 방제 업무를 위한 유용한 정보이다. 그러나 해상교통 및 안전 분야 종사자는 전통적으로 해도 및 전자해도 사용에 익숙하여 현 환경민감지도의 색상 및 심볼의 낮은 친숙도가 지적된 바 있다. 본 연구에서는 전자해도의 제작 표준에 해당하는 수로데이터 표준모델에 따라 환경민감지도 데이터를 제작하고 전자해도 표현방법에 따라 표시 하였다. 세부 연구 내용으로 환경민감정보에 대한 객체와 속성, 표현 심볼 및 색상에 대해 정의하고, 기존 환경민감정보를 내부 전자해도 포맷으로 변환하였다. 다음으로 내부 전자해도 데이터를 전자해도 표현방법에 따라 전자해도 레이어에 중첩시켜 그 결과를 확인 하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1998.11a
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• pp.428-433
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• 1998

국제적으로 개발되고 있는 전자해도는 선박의 대형화 고속화로 인한 각종해난사고의 예방과 급증하는 해상교통을 효율적으로 처리하기 위한 안전항해시스템의 기본데이터로 사용된다. 국제기구인 IHO는 1996년에 전자해도관련 국제표준을 제정하고 각 회원국에 전자해도 개발을 강력히 권고하였으며 이에 따라 국내에서도 국립해양조사원이 1999년 완료를 목표로 전자해도 개발을 추진하고 있다. 전자해도의 개발과정은 기존의 종이해도를 디지틀화 하는 것에서 출발하며 이 과정에서 CIS를 이용한다. 국내에서는 수로데이터의 처리에 강력한 기능을 갖고 있는 CARIS를 사용하여 수치해도를 제작하고 이 데이터를 기본으로 전자해도 데이터를 생성한다. 실제로 종이해도를 스캔한 이미지 파일을 벡터화하고 각종 관련 규약에 맞게 편집하여 수치해도를 제작하며 이를 바탕으로 S-57 기반의 전자해도를 제작한다. 이 과정에서 CARIS는 이미지의 벡터화, 각종 심볼의 입력, 좌표변환, 오브젝트의 입력ㆍ수정등을 수행한다. 본 논문에서는 KRISO가 1995년 말부터 국립해양조사원에서 위탁받아 연구한 전자해도 개발과정과 검수과정을 소개하고 그 과정에서 사용된 CARIS의 활용에 대해 설명한다. 이러한 고찰을 통해 향후 중요하계 다루어질 전자해도의 공급 및 관리시스템의 GIS 활용을 고려할 수 있을 것이다.

• Journal of Korean Society of Transportation
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• v.25 no.1 s.94
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• pp.23-35
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• 2007

Freeway diverging areas are very vulnerable to traffic accidents due to abrupt vehicle speed changes and geometric changes. Therefore, in designing diverging areas, much attention should be Paid to safety The Present design criteria about freeway diverging areas regulate transition sections for lane changes, deceleration lanes, transition corves for direction changes. and other similar items. However, the design criteria were often violated in implementation because of ambiguities in the criteria. This study aims at clarifying and improving the present design criteria for freeway diverging areas. For this, field survey data and traffic accident data for diverging areas were analyzed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.275-276
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• 2022

VTS operators instruct ships to wait for entry and departure to sail in one-way to prevent ship collision accidents in ports with narrow routes. Currently, the instructions are not based on scientific and statistical data. As a result, there is a significant deviation depending on the individual capability of the VTS operators. Accordingly, this study built a 1d-convolutional neural network model by collecting ship and weather data to predict the exact travel time for ship entry/departure waiting for instructions in the port. It was confirmed that the proposed model was improved by more than 4.5% compared to other ensemble machine learning models. Through this study, it is possible to predict the time required to enter and depart a vessel in various situations, so it is expected that the VTS operators will help provide accurate information to the vessel and determine the waiting order.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.65-71
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• 2011

In modem society, car are the most important transportation. Thereby, car accidents has been increasing steadily. The driver is the biggest factor of car accident. Therefor, various studies about driver (reaction time, mentality, physiological signal, age, pattern of drive) are underway. In this paper, we design a embedded system for measuring the reaction time by driving skill. The proposed system is composed of measuring brake module, OBD-2 scanner and bluetooth transmission module. Also, we implement GUI program to analyze experiment result and database to store results. Though our proposed system, we can analyze driving skill.

• Annual Conference of KIPS
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• 2022.11a
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• pp.851-853
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• 2022

고령화로 인해 65세 이상 운전자가 급증하며 고령운전자의 교통사고 비율이 증가함에 따라 시급한 사회 문제로 떠오르고 있다. 이에 본 연구에서는 객체 검출, 인식 모델을 결합하고 신호등을 인식하여 Text-To-Speech(TTS)로 알리는 쿠버네티스 기반의 프레임워크를 제안한다. 객체 검출 단계에서는 YOLOv5 모델들의 성능을 비교하여 활용하였으며 객체 인식 단계에서는 C-RNN 기반의 attention-OCR 모델을 활용하였다. 이는 신호등의 내부 LED 영역이 아닌 이미지 전체를 인식하는 방식으로 오탐지 요소를 낮춰 인식률을 높였다. 결과적으로 1,628장의 테스트 데이터에서 accuracy 0.997, F1-score 0.991의 성능 평가를 얻어 제안한 프레임워크의 타당성을 입증하였다. 본 연구는 후속 연구에서 특정 도메인에 딥러닝 모델을 한정하지 않고 다양한 분야의 모델을 접목할 수 있도록 하며 고령 운전자 및 신호 위반으로 인한 교통사고 문제를 예방할 수 있다.

• Journal of Korean Society of Disaster and Security
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• v.9 no.2
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• pp.15-21
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• 2016

According to the disaster statistics issued by the Ministry of Public Safety and Security, traffic accidents, fire, collapses and others are classified into twenty-three (23) categories. In the past, disasters were mainly caused by the influence of nature, such as typhoon or drought. On the other hand, as society has become city-centered, social disasters' types, frequencies and scales are becoming more diversified and ever-increasing. However, there are no specific criteria and assessment methods that can measure degrees of social disasters-related risks objectively. Therefore, this study targeted traffic accidents, fire and collapses from major social disasters, utilized data that are related to occurrence rate, scale of casualties and scale of property loss in past eight years, and calculated the disaster risk index using the distance (Euclidean distance) between two points on the 3D spatial coordinates, in order to make the objective assessment by social disaster type possible. These results will enable the objective evaluation of risk index of major social disaster to be used as the foundational data when building the national disaster management system.