• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.132-145
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• 2022

This study was based on the black box images of traffic accidents on highways, cluster analysis and prediction model comparisons were carried out. As analysis data, vehicle driving behavior and road surface conditions that can grasp road and traffic conditions just before the accident were used as explanatory variables. Considering that traffic accident data is affected by many factors, cluster analysis reflecting data heterogeneity is used. Each cluster classified by cluster analysis was divided based on the ratio of the severity level of the accident, and then an accident prediction evaluation was performed. As a result of applying the Logit model, the accident prediction model showed excellent predictive ability when classifying groups by cluster analysis and predicting them rather than analyzing the entire data. It is judged that it is more effective to predict accidents by reflecting the characteristics of accidents by group and the severity of accidents. In addition, it was found that a collision accident during stopping such as a secondary accident and a side collision accident during lane change act as important driving behavior variables.

• Proceedings of KOSOMES biannual meeting
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• 2005.05a
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• pp.63-66
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• 2005

목포항내 해상안전을 도모하기 위한 해상교통시스템을 설계하기 위해서는 기존의 해양사고의 현황과 유형을 분석하여 그 방지대책을 새로운 해상교통 시스템에 반영할 필요가 있다. 본 연구에서는 목포항계 내 해역 및 목포구 근방 해역을 대상으로 하여, 최근 10년간(1994. 01.$\~$2003. 12.) 발생한 해양사고 현황을 분석하여 그 방지방안을 제시하고자 한다.

• Knowledge Management Research
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• v.25 no.1
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• pp.217-233
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• 2024

The demand for two-wheelers has increased in recent years, driven by the growing delivery culture, which has also led to a rise in the number of two-wheelers. Although two-wheelers are economically efficient in congested traffic conditions, reckless driving and ambiguous traffic laws for two-wheelers have turned two-wheeler accidents into a significant social issue. Given the high fatality rate associated with two-wheelers, the severity and risk of two-wheeler accidents are considerable. It is, therefore, crucial to thoroughly understand the characteristics of two-wheeler accidents by analyzing their attributes. In this study, the characteristics of two-wheeled vehicle accidents were categorized using the K-prototypes algorithm, based on data from two-wheeled vehicle accidents. As a result, the accidents were divided into four clusters according to their characteristics. Each cluster showed distinct traits in terms of the roads where accidents occurred, the major laws violated, the types of accidents, and the times of accident occurrences. By tailoring enforcement methods and regulations to the specific characteristics of each type of accident, we can reduce the incidence of accidents involving two-wheelers in metropolitan areas, thereby enhancing road safety. Furthermore, by applying machine learning techniques to urban transportation and safety, this study adds to the body of related literature.

• Journal of Korean Society of Transportation
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• v.29 no.3
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• pp.31-40
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• 2011

Generally, inappropriate driving conditions including geometric, traffic environmental, and driver psychological problems may be critical reasons of traffic accidents. Under this circumstance, various types of facilities have been installed to improve traffic safety by itself or as a set consisting of several other traffic safety facilities. In general, traffic accidents occur by several reasons combined rather than only a single reason, and thus the safety effect of the safety facilities cannot be simply analyzed with only a single improvement. For the study, traffic accident data on the roadway segments of interest are collected along with field survey data. For the analysis, various alternative analysis methods were evaluated in terms of assessing accident reduction from various types of traffic safety improvements. Among the alternative methods tested including simple before-and-after evaluation method, before-and-after evaluation yoked comparison, and Comparison Group (C-G) method, it was found that the C-G method is the most effective method for analyzing the traffic safety improvement effect. Adopting the C-G method, both single and multiple safety improvements were analyzed. The results from this study can potentially be applied to decide the best type of treatments to improve traffic safety as well as to measure the accident reduction effects from the treatments.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.266-283
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• 2022

With the rapid development of autonomous vehicle technology, unexpected accidents are occurring. Therefore, it is necessary to minimize user accident damage through the development of autonomous traffic safety education. Since edge cases, accident type, and risk factor analysis are important for realistic education, overseas case studies and demonstrations were carried out, and based on this, two curriculum for service providers and general users were developed. The service provider curriculum consisted of OEDR, sudden stop, cut-in, take-over, defensive driving, system malfunction, policy and information security education, and the general user curriculum consisted of attention duty, take-over, operating design domain, accidents type, laws, functions, information security education.

• Journal of Korean Society of Transportation
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• v.10 no.3
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• pp.59-74
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• 1992

The change of industrial structure, the expansion of economic scale, the elevation of national life level and rapid motorization bring about social problems that are traffic accident, traffic congestion, etc. Especially, big traffic accidents bring about the great number of dead and wounded person with damage of property. But there are no study results available mainly focused on the big traffic accident analysis. Accordingly, this study is essentially for the factor analysis of big accident types including laws violation and the assaulting driver's death using Quantification ll method.

• Journal of the Society of Disaster Information
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• v.16 no.2
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• pp.310-317
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• 2020

Purpose: In this study, the characteristics of hazardous chemical transport vehicle accidents during the last six years from 2014 to 2019 was analyzed. This study is to prevent chemical transport accidents. Method: Using statistics from the Ministry of Environment, chemical transport vehicle accidents were classified into accident type, accident cause and vehicle type. Result: Of the total 506 cases chemical accidents, 105 cases were caused by transport vehicle. More than 20 percent of total accidents were reported. Most of the accidents were caused by spill·leak. The main causes of the accident were traffic accidents(49 cases) and management negligence(29 cases). This was more than 74% of all transport vehicle accidents. By vehicle type, 57 cases(54%) were most common in tank lorries, followed by trucks (39 cases, 37%) and trailers (9 cases, 9%). Conclusion: In order to prevent accident of transport vehicle, thorough inspection of the transport vehicle is required and safe operation of the driver. In addition, the government needs to expand real-time monitoring of transport vehicles and comprehensively manage different systems by the relevant ministries.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2152-2160
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• 2010

This study was attempted in order to examine relationship of elementary school students' safety consciousness upon risk recognition of accidental type. We carried out targeting 378 elementary school students who attended 4 schools in J city and K city of J province from June 25 July 15, 2009. The statistical processing was done by SPSS WIN 12.0 Version. The statistical significance was set to p<.05. As for correlation between variables in safety characteristics and safety consciousness, and risk recognition of accidental type, the safety consciousness showed positive correlation with risk recognition of traumatic accident(r=.179, p<.01) and risk recognition of environmental accident(r=.104, p<.05).The whole data of risk recognition of accidental type, the safety education within 1 year($\beta=.139$, t=2.654, p<.01) and traffic safety consciousness($\beta=.172$, t=2.714, p<.01) were indicated to had significant influence upon the risk recognition of accidental type.

• Journal of Korean Society of Transportation
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• v.23 no.1
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• pp.33-44
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• 2005

Railroad crossing crashes are fewer than road crashes, but with regard to crash severity, they can be serious injury crashes. There should be, therefore, enormous efforts to increase the safety of railroad crossings. The objective of this paper is to identify and understand factors associated with railroad crossing crashes. Statistical models are used to examine the relationships between crossing accidents and geometric elements of crossings. The results show the Poisson model is the most appropriate method for the crossing accidents, because overdispersion was not observed. This study identifies seven significant factors associated with railroad crossing crashes through the main and variant models. With regard to explanatory factors on crossing safety, the total traffic volume, daily train volume, presence of commercial area around crossings, distance of train detector from crossings, time duration between the activation of warning signals and gates, crossing types, and speed hump were found to affect the safety of railroad crossings.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.139-150
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• 2008

In this study, It is focused on development of the forecasting model about trumpet InterChange(IC) ramp accident because of the frequency of accident in ramp more than highway basic section and trend the increasing accident in ramp. The independent variables was selected through statistical analysis(correlation analysis, multi-collinearity etc) by ramp types(direct, semi-direct and loop). The independent variables and accident rate is non-linear relationship. So it made new variables by transformation of the independent variables. The forecasting models according to exit-ramp type (direct, semi-direct and loop) are built with statistical multi-variable regression using all possible regression method. And the forecasts of the models showed high accuracy statistically. It is expected that the developed models could be employed to design trumpet IC ramp more cost-efficiently and safely and to analyze the causes of traffic accidents happened on the IC ramp.