• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.156-168
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• 2024

Purpose: The trend of highway traffic accidents shows a repeating pattern of increase and decrease, with the fatality rate being highest on highways among all road types. Therefore, there is a need to establish improvement measures that reflect the situation within the country. Method: We conducted accident severity analysis using Random Forest on data from accidents occurring on 10 specific routes with high accident rates among national highways from 2019 to 2021. Factors influencing accident severity were identified. Result: The analysis, conducted using the SHAP package to determine the top 10 variable importance, revealed that among highway traffic accidents, the variables with a significant impact on accident severity are the age of the perpetrator being between 20 and less than 39 years, the time period being daytime (06:00-18:00), occurrence on weekends (Sat-Sun), seasons being summer and winter, violation of traffic regulations (failure to comply with safe driving), road type being a tunnel, geometric structure having a high number of lanes and a high speed limit. We identified a total of 10 independent variables that showed a positive correlation with highway traffic accident severity. Conclusion: As accidents on highways occur due to the complex interaction of various factors, predicting accidents poses significant challenges. However, utilizing the results obtained from this study, there is a need for in-depth analysis of the factors influencing the severity of highway traffic accidents. Efforts should be made to establish efficient and rational response measures based on the findings of this research.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.100-118
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• 2021

In this study, PM accidents (1,603case) and bicycle accidents (14,672case) that occurred in the last three years were analyzed to determine the characteristics of PM traffic accidents. In particular, PM traffic accidents were divided into perpetrators and victims to determine the characteristics in detail. For PM accidents, the analysis was conducted on the status of each road grade, road type, weather condition, accident type, day and night occurrence, and vehicle type. The number of PM accidents that occurred in 2019 increased by 129%, and deaths increased by more than 200% compared to the previous year. The proportion of pedestrian accidents among PM traffic accidents was higher than that of bicycle accidents. Therefore, regulations on PM traffic are necessary. For the 20 deaths of PM, a detailed analysis was conducted to analyze the factors of traffic accidents. PM fatalities occurred in 50% of vehicle accidents, and 7 out of 10 vehicle accidents occurred at night. This is believed to have been caused by falling or overturning due to an obstacle, such as a depression in the road pavement or a speed bump.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.173-185
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• 2015

This study aims to investigate the characteristics and types of car accidents and establish a prediction model by analyzing 456 car accidents having occurred in the 11 tunnels in Busan, through statistical analysis techniques. The results of this study can be summarized as below. As a result of analyzing the characteristics of car accidents, it was found that 64.9% of all the car accidents took place in the tunnels between 08:00 and 18:00, which was higher than 45.8 to 46.1% of the car accidents in common roads. As a result of analyzing the types of car accidents, the car-to-car accident type was the majority, and the sole-car accident type in the tunnels was relatively high, compared to that in common roads. Besides, people at the age between 21 and 40 were most involved in car accidents, and in the vehicle type of the first party to car accidents, trucks showed a high proportion, and in the cloud cover, rainy days or cloudy days showed a high proportion unlike clear days. As a result of analyzing the principal components of car accident influence factors, it was found that the first principal components were road, tunnel structure and traffic flow-related factors, the second principal components lighting facility and road structure-related factors, the third principal factors stand-by and lighting facility-related factors, the fourth principal components human and time series-related factors, the fifth principal components human-related factors, the sixth principal components vehicle and traffic flow-related factors, and the seventh principal components meteorological factors. As a result of classifying car accident spots, there were 5 optimized groups classified, and as a result of analyzing each group based on Quantification Theory Type I, it was found that the first group showed low explanation power for the prediction model, while the fourth group showed a middle explanation power and the second, third and fifth groups showed high explanation power for the prediction model. Out of all the items(principal components) over 0.2(a weak correlation) in the partial correlation coefficient absolute value of the prediction model, this study analyzed variables including road environment variables. As a result, main examination items were summarized as proper traffic flow processing, cross-section composition(the width of a road), tunnel structure(the length of a tunnel), the lineal of a road, ventilation facilities and lighting facilities.

• Journal of Navigation and Port Research
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• v.30 no.1 s.107
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• pp.35-43
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• 2006

This study suggests strategies to prevent traffic accidents by utilizing impact factors per each cluster and the typical patterns of 81 cities based on the statistical analysis of the data concerning the TCI which was developed from the partnership of the Traffic Safety Authority and the Green Traffic Movement Corporation in 2002 and 2003. The Principal Component Analysis and Cluster Analysis on impact factors and TCI result in 4 components and 4 clusters. Also as the results of Stepwise Multiple Regression Analysis examining the relationship between impact factors and TCI, R2 values of these models show high to all clusters. According to the results, we suggest strategies to prevent traffic accidents per cluster concretely and it is necessary to analyze how effective the invested facilities are in reducing traffic accidents in the future.

• Safety and Health at Work
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• v.9 no.4
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• pp.454-461
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• 2018

Background: Working conditions and psychosocial work factors have acquired an important role explaining the well-being and performance of professional drivers, including those working in the field of public transport. This study aimed to examine the association between job strain and the operational performance of public transport drivers and to compare the expositions with psychosocial risk at work of three different types of transport workers: taxi drivers, city bus drivers, and interurban bus drivers. Method: A sample of 780 professional drivers was drawn from three transport companies in Bogota (Colombia). The participants answered the Job Content Questionnaire and a set of sociodemographic and driving performance questions, including age, professional driving experience, work schedules, and accidents and penalties suffered in the last 2 years. Results: Analyses showed significant associations between measures of socio-labor variables and key performance indicators such road traffic accidents and penalties. Furthermore, multiple linear regression analysis contributed to explain significantly suffered accidents from key variables of the Job Demand-Control model, essentially from job strain. In addition, throughout post-hoc analyses, significant differences were found in terms of perceived social support, job strain, and job insecurity. Conclusion: Work stress is an issue that compromises the safety of professional drivers. This research provides evidence supporting a significant effect of job strain on the professional driver's performance. Moreover, the statistically significant differences between taxi drivers, city bus drivers, and interurban bus drivers in their expositions to work-related stress suggest the need for tailored occupational safety interventions on each occupational group.

• Journal of Trauma and Injury
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• v.25 no.4
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• pp.132-138
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• 2012

Purpose: Trauma is one of the major causes of maternal and fetal mortality, and the most common cause of maternal trauma is a traffic accident. In Korea, data about traffic accidents in pregnant women are not widely collected and classified so far. Hence, we studied and analyzed the characteristics of injured pregnant women by the traffic accidents. Methods: From January 2002 to August 2011, pregnant women who were in traffic accidents visiting Emergency Department were studied. Pregnancy out come and the degree of the damage were determined through the retrospective analysis of the medical records. Results: The pregnant women who visited after traffic accidents were total 204 patients. Among them, 176 patients had no complication related to the traffic accidents, 28 patients had complications. The incidence of the complications in the 3rd trimester pregnants was statistically significant higher than that in the other trimesters. The analysis based on the mechanism shows more complications in the pedestrian injury. In the survey by the type of the vehicles, the complications from the trauma associated with a car had lower incidence. The patients arrived at the emergency center by walking had greater numbers than who arrived by an ambulance in the groups occurred the complications. The patients suffered complications who complained pain in trunk especially in abdomen and pelvis than in extremities and complained vaginal discharge, and those showed a statistically significant greater incidence. Conclusion: When pregnant women were injured by the traffic accidents, the factors related to the poor pregnant prognosis were trimester of pregnancy, means of visiting the emergency center, trauma mechanism, and complaining symptoms. Therefore, these factors may be used as a prognostic tool to predict an incidence of complications, length of hospital stay and rate of complications and can be used to plan for treatments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.351-360
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• 2010

In 2010, the number of registered vehicles reached almost at 17.48 millions in Korea. This dramatic increase of vehicles influenced to increase the number of traffic accidents which is one of the serious social problems and also to soar the personal and economic losses in Korea. Through this research, an enhanced intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network will be developed in order to obtain the important data for developing the countermeasures of traffic accidents and eventually to reduce the traffic accidents in Korea. Firstly, this research has investigated the influencing factors of road geometric features on the traffic volume of each approaching for the intersections where traffic accidents and congestions frequently take place and, a linear regression model of traffic accidents and traffic conflicts were developed by examining the relationship between traffic accidents and traffic conflicts through the statistical significance tests. Secondly, this research also developed an intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network through applying the intersection traffic volume, the road geometric features and the specific variables of traffic conflicts. Lastly, this research found out that the developed model is better than the existed forecasting models in terms of the reliability and accuracy by comparing the actual number of traffic accidents and the predicted number of accidents from the developed model. In conclusion, it is expect that the cost/effectiveness of any traffic safety improvement projects can be maximized if this developed intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network use practically at field in the future.

• Journal of Korean Society of Transportation
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• v.29 no.3
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• pp.93-101
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• 2011

In 2008, Korea has observed total 215,822traffic accidents Although the number has decreased since then, the crash rate is still higher than those of other advanced countries. In particular, high rate of pedestrian accidents occurred on urban streets is recognized as a serious problem. The previous studies, however, are not entirely considerate of accident factors by accident type. Inspired by the fact, this study analyzes factors affecting traffic accident by accident type. Using the accident data collected on urban streets in Seodaemun-gu, this paper classifies the accidents into two groups (i.e., vehicle-vs-vehicle and vehicle-vs-person crashes), and analyzes relationships between severity and exogenous variables. For the analysis, Structural Equation Modeling (SEM) is employed to estimate relationships among exogenous factors of traffic accident by each type on urban streets. The resulting model reveals that roadway related factors are highly correlated with the severity of vehicle-vs-vehicle crashes whereas environment factors are with vehicle-vs-person crashes.

• Sleep Medicine and Psychophysiology
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• v.1 no.1
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• pp.68-75
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• 1994

In order to examine the effects of circadian rhythm in subjective alertness on the occurrence of traffic accidents, the authors investigated the occurrence rate of traffic accidents adjusted to traffic volume in Seoul and the relative rate of fatal accidents versus total traffic accidents in Korea at different times of day in 1991. We analyzed these data in relation with the circadian rhythm in subjective alertness. The results were as follows. Both the occurrence rate of traffic accidents adjusted to traffic volume and the relative rate of fatal accidents versus total traffic accidents were the highest at 3-4 a.m., known as the time period of the lowest subjective alertness. They were negatively correlated with subjective alertness (p<0.05, p<0.001). In conclusion, it is suggested that the circadian rhythm of subjective alertness should have a significant effect on the occurrence of traffic accidents despite many confounding factors such as driving environments.

• International Journal of Highway Engineering
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• v.14 no.1
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• pp.85-94
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• 2012

The term "incident duration time" is defined as the time from the occurrence of incident to the completion of the handling process. Reductions in incident durations minimize damages by traffic accidents. This study aims to develop models to identify factors that influence incident duration by investigating traffic accidents on highways. For this purpose, four models were established including an integrated model (Model 1) incorporating all accident data and detailed models (Model 2, 3 and 4) analyzing accidents by location such as basic section, bridges and tunnels. The result suggested that the location of incident influences incident duration and the time of arrival of accident treatment vehicles is the most sensitive factor. Also, significant implications were identified with regard to vehicle to vehicle accidents and accidents by trucks, in night or in weekends. It is expected that the result of this study can be used as important information to develop future policies to manage traffic accidents.