• International Journal of Highway Engineering
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• v.20 no.2
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• pp.119-126
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• 2018

PURPOSES : The purpose of this study is to develop a crash prediction model at signalized intersections, which can capture the randomness and uncertainty of traffic accident forecasting in order to provide more precise results. METHODS : The authors propose a random parameter (RP) approach to overcome the limitation of the Count model that cannot consider the heterogeneity of the assigned locations or road sections. For the model's development, 55 intersections located in the Daejeon metropolitan area were selected as the scope of the study, and panel data such as the number of crashes, traffic volume, and intersection geometry at each intersection were collected for the analysis. RESULTS : Based on the results of the RP negative binomial crash prediction model developed in this study, it was found that the independent variables such as the log form of average annual traffic volume, presence or absence of left-turn lanes on major roads, presence or absence of right-turn lanes on minor roads, and the number of crosswalks were statistically significant random parameters, and this showed that the variables have a heterogeneous influence on individual intersections. CONCLUSIONS : It was found that the RP model had a better fit to the data than the fixed parameters (FP) model since the RP model reflects the heterogeneity of the individual observations and captures the inconsistent and biased effects.

• Journal of the Korean Society of Safety
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• v.26 no.4
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• pp.127-134
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• 2011

A traffic accident which happens in Expressway during dense fog is more likely to cause the sequential accidents and high death rate. So, the preventive measures shall be taken at dangerous areas to enhance the efficiency of roads and minimize the accidents and the resultant damages. So, it is necessary to find out the characteristics of freeway zone which has high risk of fog occurrence and to establish the comprehensive safety strategy on installation and operation of the safety equipment. In this study, I developed a fog forecasting model by using the freeway fog data. This model can be used as the fog forecasting model in dealing with fog problems when new road is planned. The model was developed by using a statistical analysis technique or the regression analysis, focusing on the variables such as geographical features and regional conditions, distances to water sources and the area of water source. I have segmented the models by classifying the area into inland area and coastal area. The distance to water source and area of the water source located around the freeway were found to be main factors causing fog.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.17-24
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• 2008

Traffic accident forecasting model has been developed steadily to understand factors affecting traffic accidents and to reduce them. In Korea, the length of highways is over 3,000km, and it is within the top ten in the world. However, the number of accidents-per-one kilometer highway is higher than any other countries. The rapid increase of travel demand and transportation infrastructures since 1980's may influence on the high rates of traffic accident. Accident severity is one of the important indices as well as the rate of accident and factors such as road geometric conditions, driver characteristics and type of vehicles may be related to traffic accident severity. However, since all these factors are interacted complicatedly, the interactions are not easily identified. A structural equations model is adopted to capture the complex relationships among variables. In the model estimation, we use 2,880 accident data on highways in Korea. The SEM with several factors mentioned above as endogenous and exogenous variables shows that they have complex and strong relationships.

• Fire Science and Engineering
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• v.25 no.6
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• pp.32-38
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• 2011

As the using rate of an explosive gas has been increased in the industrial site, the regional residents adjacent to the site as well as the site workers have frequently fallen into a dangerous situation. Damage caused by accident in the process using hydrogen gas is not confined only to the relevant process, but also is linked to a large scale of fire or explosion and it bring about heavy casualties. Therefore, personnel in charge should investigate the kinds and causes of the accident, forecast the scale of damage and also, shall establish and manage safety countermeasures. We, in Anti-Calamity Research Center, forecasted the scope of danger if break out a fire or/and explosion in hydrogen gas facilities of MLCC firing process. We selected piping leak accident, which is the most frequent accident case based on an actual analysis of accident data occurred. We select and apply piping leak accident which is the most frequent case based on an actual accident data as a model of damage forecasting scenario caused by accident. A jet fire breaks out if hydrogen gas leaks through pipe size of 10 mm ${\Phi}$ under pressure of 120 bar, and in case of $4kw/m^2$ of radiation level, the radiation heat can produce an effect on up to distance of maximum 12.45 meter. Herein, we are going to recommend safety security and countermeasures for improvement through forecasting of accident damages.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.4
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• pp.1-6
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• 2004

For a given plant design, improved decisions on when to shutdown an existing plant may be obtained by making better predictions of failure rates, by exerting efforts to collect more relevant information or by improving decision making models which put that information to best use. It is important that the models include the value of possible loss of lives and fear along with cleanup, decommissioning, relocation if the decisions derived from the model are to be useful. The decision model we have described enables us to investigate a class of optimal decisions on whether to shutdown or continue operating one period of time. The analysis and decision process is repeated at the end of each period with additional information about new costs and risks.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.99-110
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• 2008

It is commonly estimated that there is a much higher potential for accidents at a crossroads than along a single road due to its plethora of conflicting points. According to the 2006 figures by the National Police Agency, the number of traffic accidents at crossroads is greatly increasing compared to that along single roads. Among others, crossroads installed with traffic signals have more varied influential factors for traffic accidents and leave much more room for improvement than ones without traffic signals; thus, it is expected that a noticeable effect could be achieved in safety if proper counter-measures against the hazards at a crossroads were taken together with an estimate of causes for accidents This research managed to develop models for accident forecasts and accident intensity by applying data on accident history and site inspection of crossroads, targeting four selected downtown crossroads installed with traffic signals. The research was done by roughly dividing the process into four stages: first, analyze the accident model examined before; second, select variables affecting traffic accidents; third, develop a model for traffic accident forecasting by using a statistics-based methodology; and fourth, carry out the verification process of the models.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of Korean Society of Transportation
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• v.21 no.1
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• pp.21-40
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• 2003

A traffic accident analysis method was developed and tested based on the highway alignment risk indices using geographic information systems(GIS). Impacts of the highway alignment on traffic accidents have been identified by examining accidents occurred on different alignment conditions and by investigating traffic accident risk indices(TARI). Evaluative criteria are suggested using geometric design elements as an independent variable. Traffic accident rates were forecasted more realistically and objectively by considering the interaction between highway alignment factors and the design consistency. And traffic accident risk indices and risk ratings were suggested based on model estimation results and accident data. Finally, forecasting traffic accident rates, evaluating the level of risk and then visualizing information graphically were combined into one system called risk assessment system by means of GIS. This risk assessment system is expected to play a major role in designing four-lane highways and developing remedies for highway sections susceptible to traffic accidents.

• International Journal of Highway Engineering
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• v.13 no.3
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• pp.157-165
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• 2011

With respect to the trend of highway traffic accident, highway accident is in decline, whileas, the fatality is on an increasing trend. Thus, many efforts to decrease highway traffic accidents and improve the safety, are required. In particular, in case of highway, the management standard by grade for accident black spot is designated. Thus, investing the effect factors by grade for highway traffic accident is required in detail. Thus, in this study, the factors affecting the traffic accidents among the environmental factors based on the graded data for the accident black spot in the applicable section targeting the Seoul-Pusan Express Highway, were reviewed; accident forecasting model which would analyze the characteristics of the accidents for determining the accident grade, was developed. As a result of establishing a model by using Quantification Theory of Type II, considering the characteristics of the dependent and independent variables based on the geometric structure, 'the fixed variable' among the variables relating to the accident, for the variables influencing over the accident grade, 'the type of vans, a chassis and people', 'the trailers, special vehicles and chassis people' and 'the negligence of watching and cloudy weather' were analyzed as common factors, in case of 'horizontal alignment', 'longitudinal slope' and, 'barricade' respectively.

• Journal of the Korea Institute of Building Construction
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• v.23 no.6
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• pp.831-839
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• 2023

This study aims to develop a framework for an accident prediction model leveraging a deep neural network algorithm, specifically tailored for small-scale construction sites. Notably, the incidence of accidents in the construction sector is markedly higher compared to other industries, with a significant contribution from small-scale sites. The challenging nature of construction in urban settings, coupled with the increasing frequency of adverse weather conditions, is likely to escalate accident risks at these sites. Anticipating and mitigating accidents at small-scale construction sites is therefore crucial to decrease the overall industry accident rate. Consequently, this research introduces a Deep Neural Network-based model for forecasting accidents at small-scale construction sites. The framework and findings of this study are poised to serve as a guideline for the safety management of small-scale construction projects, ultimately aiding in the realization of safer, more sustainable construction practices at these sites.