• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.679-690
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• 2015

The need to remove the cause of traffic accidents by improving the engineering system for a vehicle and the road in order to minimize the accident hazard. This is likely to cause traffic accident continue to take a large and significant social cost and time to improve the reliability and efficiency of this generally poor road, thereby generating a lot of damage to the national traffic accident caused by improper environmental factors. In order to minimize damage from traffic accidents, the cause of accidents must be eliminated through technological improvements of vehicles and road systems. Generally, it is highly probable that traffic accident occurs more often on roads that lack safety measures, and can only be improved with tremendous time and costs. In particular, traffic accidents at intersections are on the rise due to inappropriate environmental factors, and are causing great losses for the nation as a whole. This study aims to present safety countermeasures against the cause of accidents by developing an intersection Traffic safety evaluation model. It will also diagnose vulnerable traffic points through BPA (Back -propagation algorithm) among artificial neural networks recently investigated in the area of artificial intelligence. Furthermore, it aims to pursue a more efficient traffic safety improvement project in terms of operating signalized intersections and establishing traffic safety policies. As a result of conducting this study, the mean square error approximate between the predicted values and actual measured values of traffic accidents derived from the BPA is estimated to be 3.89. It appeared that the BPA appeared to have excellent traffic safety evaluating abilities compared to the multiple regression model. In other words, The BPA can be effectively utilized in diagnosing and practical establishing transportation policy in the safety of actual signalized intersections.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.537-546
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• 2020

This study deals with the development and improvement of a particle dispersion model for quick response to water pollutant accidents. The developed model is based on the shear dispersion theory where vertical mixing is done by step by step mixing by vertical and molecular diffusion algorithm. For the quick response to chemical accidents, an algorithm for multi-core modeling for the particle dispersion model is applied. After the application of multi-core operation using OpenMP directives to the model, the relation for the calculation time and particle size were determined along with the number of cores used for parallel programming to determine the model time for chemical accident responses. The results showed the adequate conditions for the modeling of chemical accidents for quick response and to increase the applicability of the model.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.662-674
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• 2014

In this study, accident frequency prediction models were constructed by collecting variables such as geometric structures, safety facilities, traffic volume and weather conditions, land use, highway design-satisfaction criteria along 780km (4,372 sections) of 4 lane-highways over 8 areas. As for models, a fixed parameter model and a random parameter model were employed. In the random parameter model, some influences were reversed as the range was expressed based on specific probability in the case of no fixed coefficients. In the fixed parameter model, the influences of independent variables on accident frequency were interpreted by using one coefficient, but in the random parameter model, more various interpretations were took place. In particular, curve radius, securement of shoulder lane, vertical grade design criteria satisfaction showed both positive and negative influence, according to specific probability. This means that there could be a reverse effect depending on the behavioral characteristics of drivers and the characteristics of highway sections. Rather, they influence the increase of accident frequency through the all sections.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.07a
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• pp.72-73
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• 2015

진도 앞바다에서 발생한 세월호의 사고원인 규면의 일환으로 세월호와 같은 모델을 제작하여 자유항주 실험을 실시하였다. 현재 세월호의 정확한 GM은 알 수 없고 추정치만 제시되고 있는 상황이다. 가장 객관적인 자료로는 세월호의 AIS항적이 유일하다고 볼 수 있다. 이에 사고당시의 세월호와 동일한 AIS 항적으로 이동할 수 있는 조건을 검토하여 자유항주 실험을 실시하였다. 그 결과 사고 당시 세월호의 AIS 항적과 유사한 자유항주 모형선의 실험결과를 얻을 수 있었다.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.81-89
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• 2015

Crashes that occur at tollgates have different characteristics compared to those of the mainline on expressways in terms of crash cause, crash type, and vehicle type. Due to this fact, the safety performance function (SPF) focused on the expressway tollgates, apart from the mainline, should be developed. The aim of this study is, therefore, to identify the influential factors and develope a SPF for crashes at tollgates. Firstly, we established independent variables affecting crashes at tollgates through literature review and descriptive statistical analysis. Based on these variables, two negative binomial regression models with different form of independent variables were developed and goodness-of-fits of each model were compared. According to the results, the number of crashes increases i) as AADT, Hi-pass rate, and heavy vehicle rate increase, ii) as average lane width decreases, iii) on the mainline tollgate type. The safety performance function developed in this study could be applied to select hot-spots for expressway tollgates.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2437-2445
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• 2013

The objective of this study is finding the relationship between interstate highway accident frequencies and geometrics using Random Parameter Negative Binomial model. Even though it is impossible to take account of the same design criteria to the all segments or corridors on the road in reality, previous research estimated the fixed value of coefficients without considering each segment's characteristic. The drawback of the traditional negative binomial is not to explain the integrated variations in terms of time and the distinct characters specific segment has. This results in under-estimation of the standard error which inflates the t-value and finally, affects the modeling estimation. Therefore, this study tries to find the relationship of accident frequencies with the heterogeneous geometrics using 9-years and 7-interstate highway data in Washington State area. 16-types of geometrics are used to derive the model which is compared with the traditional negative binomial Model to understand which Model is more suitable. In addition, by calculating marginal effect and elasticity, heterogeneous variables' effect to the accidents are estimated. Hopefully, this study will help to estiblish the future policy of geometrics.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.369-379
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• 2014

The Statistical regression model has been used to construct crash prediction models, despite its limitations in assuming data distribution and functional form. In response to the limitations associated with the statistical regression models, a few studies based on non-parametric methods such as neural networks have been proposed to develop crash prediction models. However, these models have a major limitation in that they work as black boxes, and therefore cannot be directly used to identify the relationships between crash frequency and crash factors. A genetic programming model can find a solution to a problem without any specified assumptions and remove the black box effect. Hence, this paper investigates the application of the genetic programming technique to develope the crash prediction model. The data collected from the Gyeongbu expressway during the past three years (2010-2012), were separated into straight and curve sections. The random forest technique was applied to select the important variables that affect crash occurrence. The genetic programming model was developed based on the variables that were selected by the random forest. To test the goodness of fit of the genetic programming model, the RMSE of each model was compared to that of the negative binomial regression model. The test results indicate that the goodness of fit of the genetic programming models is superior to that of the negative binomial models.

• Journal of Korean Society of Transportation
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• v.21 no.4
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• pp.133-144
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• 2003

The current procedure of estimating accident reduction benefit shows fixed accident rates for each level of roads without considering the various characteristics of roadway geometries, and traffics. In this study, in order to solve the problems mentioned in the above, models were developed considering the characteristics of roadway alignments and traffic characteristics. The developed models can be used to estimate the accident rates on new or improved roads, In this study, only urban highways were included as a beginning stage. First of all. factors influencing accident rates were selected. Those factors such as traffic volumes. number of signalized intersections, the number of connecting roads, number of pedestrian traffic signals, existence of median barrier, and the number of road lane are also selected based upon the obtainability at the planning stage of roads. The relationship between the selected factors and accident rates shows strong correlation statistically. In this study, roads were classified into 4 groups based on number of lanes, level of roads and the existence of median barriers. The regression analysis had been performed for each group with actual data associated with traffic, roads. and accidents. The developed regression models were verified with another data set. In this study, in order to develop the proposed models, only data on a limited area were used. In order to represent whole area of the country with the developed models. the models should be re-analyzed with vast data.

• Korean Journal of Culture and Social Issue
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• v.9 no.spc
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• pp.123-147
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• 2003

This paper provides a comparative analysis of the probabilistic versus deterministic view of accident and safety using the indigenous and cultural perspectives. Death and injury due to accidents is the leading cause of preventable death in most countries, including Korea. The first part of this paper delineates the limitation of the linear, deterministic model that has been adopted in social and applied sciences. The transactional model, advocated by indigenous psychology, is provided to understand the probabilistic nature of accident and safety at home, in the workplace and in society. Second, factors related to accidents and safety are reviewed. Third, application of the probabilistic model for preventing accidents and promoting safety in Korea is outlined.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.1
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• pp.26-37
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• 2017

The study is to develop safety performance functions(SPFs) for urban intersections using random effects Tobit regression model and to analyze correlations between crashes and factors. Also fixed effects Tobit regression model was estimated to compare and analyze model validation with random effects model. As a result, AADT, speed limits, number of lanes, land usage, exclusive right turn lanes and front traffic signal were found to be significant. For comparing statistical significance between random and fixed effects model, random effects Tobit regression model of total crash rate could be better statistical significance with $R^2_p$ : 0.418, log-likelihood at convergence: -3210.103, ${\rho}^2$: 0.056, MAD: 19.533, MAPE: 75.725, RMSE: 26.886 comparing with $R^2_p$ : 0.298, log-likelihood at convergence: -3276.138, ${\rho}^2$: 0.037, MAD: 20.725, MAPE: 82.473, RMSE: 27.267 for the fixed model. Also random effects Tobit regression model of injury crash rate has similar results of model statistical significant with random effects Tobit regression model.