• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.301-310
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• 2021

The TATI model is a Traffic Accident Text to RGB Image model, which is a methodology proposed in this paper for predicting the severity of traffic accidents. Traffic fatalities are decreasing every year, but they are among the low in the OECD members. Many studies have been conducted to reduce the death rate of traffic accidents, and among them, studies have been steadily conducted to reduce the incidence and mortality rate by predicting the severity of traffic accidents. In this regard, research has recently been active to predict the severity of traffic accidents by utilizing statistical models and deep learning models. In this paper, traffic accident dataset is converted to color images to predict the severity of traffic accidents, and this is done via CNN models. For performance comparison, we experiment that train the same data and compare the prediction results with the proposed model and other models. Through 10 experiments, we compare the accuracy and error range of four deep learning models. Experimental results show that the accuracy of the proposed model was the highest at 0.85, and the second lowest error range at 0.03 was shown to confirm the superiority of the performance.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.8
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• pp.101-109
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• 2019

Korea's transportation paradigm is shifting from a vehicle-oriented transportation plan to a pedestrian-friendly environment that emphasizes walking safety. However, the level of pedestrian traffic accidents in Korea is still high and serious. The purpose of this study is to investigate factors affecting the severity of pedestrians traffic accidents using the multilevel logistic regression model based on 2015-2017 pedestrian accidents data provided by the Traffic Accident Analysis System(TAAS). The main results of the multilevel logistic regression model showed that 89% of pedestrian traffic accidents in Seoul were explained by individual characteristics such as drivers and pedestrians, and 11% were explained by neighborhood environmental characteristics. The results are as follows : In the individual characteristics such as pedestrians and drivers, the older the pedestrians and the drivers, the higher the traffic accident severity. The severity of traffic accidents was high when the pedestrians were female and the drivers were male. In the case of accident types, traffic accidents were more serious in the cases of heavy vehicles, inclement weather, and occurring at intersections and crosswalks. The results of the neighborhood environmental characteristics are as follows. The intersection density and the crosswalk density tended to reduce the severity of traffic accidents. On the other hand, the traffic light density and the school zones were founded to related to the higher level of traffic accident severity. This study suggests that both individual and neighborhood environmental characteristics should be considered together to prevent and reduce the severity of pedestrian traffic accidents.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.354-362
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• 2000

Increasing amount of road tragic in 90's has drawn much attention in Korea due to its influence on safety problems. Various types of data analyses are done in order to analyze the relationship between the severity of road traffic accident and driving conditions based on traffic accident records. Accurate results of such accident data analysis can provide crucial information for road accident prevention policy. In this paper, we apply several data fusion, ensemble and clustering algorithms in an effort to increase the accuracy of individual classifiers for the accident severity. An empirical study results indicated that clustering works best for road traffic accident classification in Korea.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.167-173
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• 2009

It is important to study on the traffic accident severity reduction because traffic accident is an issue that is directly related to human life. Therefore, this research developed countermeasure to reduce traffic accident severity considering various factors that affect the accident severity. This research developed the Accident Severity Prediction Model using the collected accident data from Seohaean Expressway in 2004~2006. Through this model, we can find the influence factors and methodology to reduce accident severity. The results show that speed limit violation, vehicle defects, vehicle to vehicle accident, vehicle to person accident, traffic volume, curve radius CV(Coefficient of variation) and vertical slope CV were selected to compose the accident severity model. These are certain causes of the severe accident. The accidents by these certain causes present specific sections of Seohaean Expressway. The results indicate that we can prevent severe accidents by providing selected traffic information and facilities to drivers at specific sections of the Expressway.

• Journal of the Korean Society of Safety
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• v.28 no.6
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• pp.84-89
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• 2013

The main causes of traffic accidents can be classified by 3 factors - human error, vehicle deficiency and road environmental problem and most accidents occurs not only 1 factor but combination of 2 or 3-factors. Among these factors, road environmental factor is the most important factor due to influence the behavior of cars and road users and road environmental factor affects 30% of total accidents approximately. The 5 years traffic accidents data analyzed to verify the accidents severity on Korea National Highways. In order to analyze the severity, Ordered Probit Model was used. As a independent variables of this model the number of lane, neighbor road environments, sight distance, vertical grade, lane width, shoulder width and traffic volume were used and as a dependent variables the minor injuries, serious injuries and fatalities were used. Research results shows that sight distance and lane width are identified as significant factors for the traffic accident severity and lesser sight distance and lane width shows greater traffic accident severity.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.841-851
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• 2019

This study presents risk ranking by accident types at intersections, crosswalk and tunnel sections. An ordered logit model was used to estimate the accident severity of traffic accidents based on 58,868 accident records that have occurred on the Seoul and Gyeonggi-do over the period 2014-2017. The factors affecting the injury severity were identified by the estimated model first, and risk ranking was proposed according to conditions of accident occurrence using relative ratio analysis later. The analysis results showed that the injury severity dramatically depends on the location and time of the accident. The analysis results showed that the injury severity dramatically depends on the location and time of the accident. Furthermore, there are severe injury cases in terms of the injury severity despite the small number of occurrence of traffic accident, or there are severe injury cases in terms of the injury severity despite the high frequency of occurrence of traffic accident.

• Smart Media Journal
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• v.5 no.4
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• pp.75-82
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• 2016

Although Korea's economic and domestic automobile market through the change of road environment are growth, the traffic accident rate has also increased, and the casualties is at a serious level. For this reason, the government is establishing and promoting policies to open traffic accident data and solve problems. In this paper, describe the method of predicting traffic accidents by eliminating the class imbalance using the traffic accident data and constructing the Hybrid Model. Using the original traffic accident data and the sampled data as learning data which use FP-Growth algorithm it learn patterns associated with traffic accident injury severity. Accordingly, In this paper purpose a method for predicting the severity of a victim of a traffic accident by analyzing the association patterns of two learning data, we can extract the same related patterns, when a decision tree and multinomial logistic regression analysis are performed, a hybrid model is constructed by assigning weights to related attributes.

• Journal of the Korean Society of Safety
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• v.27 no.4
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• pp.108-114
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• 2012

Traffic Accident Merging Index(TAMI) is developed for TMACS(Traffic Safety Information Management Complex System). TAMI is calculated by combining 'Severity Index' and 'Frequency'. This paper suggest the accurate TAMI prediction model by time series forecasting. Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. Searches the model which minimizes the error of 230 local self-governing groups. TAMI of 2007~2009 years data predicts TAMI of 2010. And TAMI of 2010 compares an actual index and a prediction index. And the error is minimized the constant where selects. Exponential Smoothing model was selected. And smoothing constant was decided with 0.59. TAMI Forecasting model provides traffic next year safety information of the local government.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.440-447
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• 2024

Purpose: The number of registered vehicles in Korea continues to increase. As traffic volume increases gradually due to improved quality of life, the severity of accidents is expected to increase and congestion problems are also expected. Therefore, it is necessary to analyze the accident factors of pointed traffic accidents and non-pointed traffic accidents. Method: The severity of the apical and non-pointed traffic accidents in Incheon Metropolitan City is analyzed by dividing them into apical and non-pointed traffic accidents to investigate the factors affecting the accident. XGBoost machine learning techniques were applied to analyze the severity of pointed and non-pointed traffic accidents and visualized as plot through the results. Result: It was analyzed that during non-peak hours, such as the case of the victim's vehicle type at peak times, the victim's vehicle type and construction machinery are variables that increase the severity of the accident. Conclusion: It is meaningful to derive the seriousness factors of apical and non-pointed accidents, and it is hoped that it will be used to reduce congestion costs by reducing the seriousness of accidents in the case of apical and non-pointed in the future.

• Journal of Korean Society of Transportation
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• v.12 no.1
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• pp.137-149
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• 1994

The objective of study is to evaluate the effect of speed control hump on traffic operation and accidents. Three sites were investigated for the change of traffic accidents before and after the hump installation. Vehicle speeds approaching the hump were also analyzed. The study revealed that not only the number of traffic accidents but also the accident severity were significantly reduced by the installation of hump. Further, different types of traffic accidents with lower severity were observed after the hump installation. For the effect of speed reduction by hump, it was found that the speeds observed at 15m upstream of hump were in the range of 36~50 percent of approaching speeds which were not affected by (ie, without) the hump. Economic analysis of hump installation showed the benefit-cost ratio of 4.3 and 11.2 at two sites. Further analysis revealed that the benefit by the accident reduction exceeds the cost by speed reduction and installation capital if AADT is below 43,150 vehicles on two lane highways. It is recommended from the study that humps should be considered on two lane highways of high accident locations for excessive speeds to reduce traffic accidents and severity.