• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.263-263
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• 2023

블랙아이스는 눈에 쉽게 구분되지 않아 많은 교통사고를 초래하고 있다. 한국교통연구원 교통사고분석시스템에 따르면, 2017년부터 2021년까지 5년간의 서리/결빙으로 인한 교통사고 사망자는 122명, 적설로 인한 교통사고 사망자는 40명으로, 블랙아이스는 적설에 비해 위험성이 높은 것으로 나타난다. 과거의 다양한 연구에서 블랙아이스 생성조건을 기압과 한기 축적등의 조건에서 예측해왔지만, 이러한 기상학적 모델은 봄철 해빙기의 일교차로 인한 눈의 해동과 재냉각과 같은 다양한 기상 조건에서의 블랙아이스 탐지가 어렵다는 한계가 있어 최근에는 이미지 판별과 딥러닝모델(YOLO 등)을 기반으로 한 센서가 제시되고 있다. 그러나, 이러한 방법은 충분한 컴퓨팅 자원이 뒷받침되어야 하며, 블랙아이스 탐지까지 걸리는 속도가 빠르지 못한 편으로, 블랙아이스 초입 구간에서의 제동에 취약하다는 잠재적인 약점을 가지고 있다. 그러므로 본 연구에서는 블랙아이스의 주 원인인 서리나 어는비가 발생하기 위해서 주변 공기가 이슬점 온도 이하, 노면온도와 이슬점이 어는점보다 낮아야 함을 이용, IoT 센서 모듈을 통해 Magnus 방정식으로 계산한 이슬점 온도와 노면 온도를 사용하는 이동식 블랙아이스 추정 장치를 제시한다. 본 장치는 대기압, 온도, 습도로부터 계산된 이슬점 온도와 노면 온도를 통한 서리발생 가능성과 대기 온도, 노면 온도를 통해 어는비의 발생환경 여부를 계산한다. 본 연구 결과를 통해 블랙아이스 추정과 기상정보 생산을 동시에 가능케 하며, 추정 결과를 통합 수집서버에 전송함으로서 운전자에게 전방 블랙아이스 위험 구간을 조기에 전달하는 시스템과 이를 관리하기 위한 인프라를 구축하여 운전 시 결빙 미끄러짐 사고를 저감하고자 한다.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.125-136
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• 2005

The current traffic accident reduction procedure in economic feasibility study does not consider the characteristics of road and V/C ratio. For solving this problem, this paper suggests methods to be able to evaluate safety of each road in construction and improvement through developing accident Prediction model in reflecting V/C ratio Per road types and traffic characters. In this paper as primary process, model is made by tke object of urban roads. Most of all, factor effecting on accident relying on road types is selected. At this point, selecting criteria chooses data obtained from road planning procedure, traffic volume, existence or non-existence of median barrier, and the number of crossing point, of connecting road. and of traffic signals. As a result of analyzing between each factor and accident. all appear to have relatives at a significant level of statistics. In this research, models are classified as 4-categorized classes according to roads and V/C ratio and each of models draws accident predicting model through Poisson regression along with verifying real situation data. The results of verifying models come out relatively satisfactory estimation against real traffic data. In this paper, traffic accident prediction is possible caused by road's physical characters by developing accident predicting model per road types resulted in V/C ratio and this result is inferred to be used on predicting accident cost when road construction and improvement are performed. Because data using this paper are limited in only province of Jeollabuk-Do, this paper has a limitation of revealing standards of all regions (nation).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6D
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• pp.587-597
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• 2010

When a car accident occurs, people who had an accident are not free from civil and criminal issues so that the accident investigator should reenact and analyze the accident situation accurately. In addition, the obtained documents through the analysis of such car accident occurrence and related factors have to be used to carry out the improvement of the areas that has numerous car accidents and complementary actions. The vehicle speed, accelerating force, braking power are currently known as the most affecting factors in accordance with many car accidents, traffic facilities, road design, etc. The vehicle's performance and rode friction coefficient road surface friction coefficient are affecting the most closely in this field. Especially, once the estimate of the speed of the accident moment relating to main eleven articles of Traffic Accident Exemption Law is very important and accuracy is required. However, currently the researches of these matters have not made exclusively yet in Korea. In this study by reflecting this current situation, until the sudden braking history is found from the car's sudden braking, it estimates accurately the transient brake time and rode friction coefficient by measuring a time of transient brake time through the precision speed detector (Vericom VC2000PC). The analysis of the experimental results calculated the transient brake time and friction coefficient to fit into the purpose of this study in the basis of different kind of various special purpose asphalt pavement and slip-prevention pavement and provided the fundamental data.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.119-130
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• 2010

Reconstruction analysis of traffic accident is done by analyzing diverse data such as the road, accident traces and damage on the automobile. Most data can be a variable in the process of analysis, and measurement error of the data occurs from the investigator, tool and the given environment. Therefore, accident analysis always has some risks of measurement uncertainty. This research quantify the uncertainty in traffic accident analysis by conducting repetitive measurement experiments for variables with high probability of uncertainly such as length (i.e. geometric structure of the road, tire marks) and coefficient of friction. This paper also suggests an analysis result for the uncertainly of photographic observation of automobile crush measurement. These statistical distributions can help determine appropriate ranges for the input data in order to estimate the accident reconstruction uncertainty.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.29-40
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• 2008

Although a good understanding of the relationship between highway traffic accidents and highway geometric features is fundamental in highway design and safety, the relationship is not well understood quantitatively. The overall goal of this paper is to formulate a reliable statistical model fitting to historical highway accident data. The model can be used to estimate the effect of road design elements on safety for the practical purposes of highway design applications. En route to achieving this goal, a number of specific research objectives were accomplished: investigate the major design elements affecting highway safety; review the existing modeling approaches in order to assess the relationship between safety and highway design features; and formulate a statistical model fitting to the accident data in order to estimate the interchange ramp junction accident frequency of rural highways.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.145-154
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• 2012

This paper analyzed the characteristics of traffic accidents in all tunnels on nationwide freeways and selected some various independent variables related to accident occurrence in tunnels. The study aims to develop reliable accident forecasting models using the various dependent variables such as the number of accident (no.), no./km, and no./MVK. Finally, reliable multiple linear regression models were proposed in this paper. This study tested the validity verification of developed models through statistics such as $R^2$, F values, multicollinearity, residual analysis. The paper selected the accident forecasting models considering the characteristics of tunnel accidents and two models were finally proposed according to two groups of tunnel length. In the selected models, natural logarithm of ln(no./MVK) is used for the dependent variable and AADT, vertical slope, and tunnel hight are used for the independent variables. The reliability of two models was proved by the comparison analysis between field data and estimating data using RMSE and MAE. These models may be not only effective in evaluating tunnel safety under design and planning phases of tunnel but also useful to reduce traffic accidents in tunnels and to manage the traffic flow of tunnel.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.224-239
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• 2018

Autonomous vehicles can significantly reduce accidents due to 'driver's carelessness', which occupies the majority of causes for traffic accidents, but they may fail to avoid traffic accidents due to unexpected situations, such as "trolley dilemma", vehicle defects and road defects. Therefore in situations Autonomous vehicles need to be made ethical choices. This study assumes that Autonomous vehicles can not avoid traffic accidents due to unexpected sink holes. In this situation, the traffic accident costs was analyzed for the ethical choices of Autonomous vehicles. In the process, Autonomous vehicles were made to choose one of three ethical choices : (1) Egoism with priority on passenger safety, (2) Deontology for minimizing human damages, (3) Utilitarianism with minimizing traffic accident costs. As a result of the analysis, egoism had the highest traffic accident costs, and deontology for minimizing human damages had the lowest traffic accident costs.

• Journal of Environmental Impact Assessment
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• v.25 no.1
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• pp.1-14
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• 2016

With the continuous expansion of highway network and its traffics, neighboring wildlife habitats are splitted into smaller and more isolated patches. The infrastructures contribute to the wildlife-vehicle collision by creating barriers to animal movement. This kinds of traffic accidents are dangerous factors to the drivers' safety and the facilities on the highway as well as to the wildlife themselves. One of the most common ways to prevent habitat fragmentation are fauna crossings and fences. The cost of the mitigation measures to prevent wildlife-vehicle collision could be monetized. However their economic benefits are difficult to be measured. Using contingent valuation method, this study tries to estimate the economic valuation of wildlife collision prevention projects on the Korean highways. The result shows that 43.88% of Korean household had the positive willingness pay to the projects. Moreover, we found that the recognition of the project or the favourable attitude to the environmental issues could raise the willingness-to-pay. Therefore, active public relation on the project could make the friendly public opinion and increase the number of the household which has the positive willingness-to-pay on the project.

• Journal of Korean Society of Transportation
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• v.32 no.1
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• pp.73-81
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• 2014

This study dealt with the impacts of geometric structure on traffic accidents occurring on the interstates. There are standard values for the case of geometric structure which are recommended in the design guideline/policy; however, in the previous models, geometric variables were adapted as integrated ones as opposed to mixed ones in the real world so that derived models had a weakness to reflect the real. Therefore, using subdivided geometric variables, this study tried to derive the model which reflects the real world. In addition, by calculating elasticity, each variables' effect to the accidents are estimated. Hopefully, this study will help to establish the future guideline/policy of geometrics considering traffic safety.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1123-1130
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• 2020

This study aims to classify the severity in car crashes using five classification learning models. The dataset used in this study contains 21,013 vehicle crashes, obtained from Korea Expressway Corporation, between the year of 2015-2017 and the LightGBM(Light Gradient Boosting Model) performed well with the highest accuracy. LightGBM, the number of involved vehicles, type of accident, incident location, incident lane type, types of accidents, types of vehicles involved in accidents were shown as priority factors. Based on the results of this model, the establishment of a management strategy for response of highway traffic accident should be presented through a consistent prediction process of accident severity level. This study identifies applicability of Machine Learning Models for Predicting of the Severity of Car Traffic Accidents on a Highway and suggests that various machine learning techniques based on big data that can be used in the future.