• International Journal of Highway Engineering
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• v.18 no.6
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• pp.173-180
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• 2016

OBJECTIVES : Fixed roadside objects are a threat to drivers when their vehicles deviate from the road. Therefore, such roadside objects need to be suitably dealt with to decrease accidents. This study determines the factors affecting the severity of accidents because of fixed roadside objects. METHODS : This study analyzed the crash severity impact of fixed roadside objects by using ordered probit regression as the analysis methodology. In this research, data from 896 traffic accidents reported in the last three years were used. These accidents consisted of sole-car accidents, fixed roadside object accidents, and lane-departure accidents on the national highway of Korea. The accident severity was classified as light injury, severe injury, and death. The factors relating to the road and the driver were collected as independent variables. RESULTS : The result of the analysis showed that the variables of the crash severity impact are the collision location (left side), gender of the driver (female), alcohol use, collision facility (roadside trees, traffic signals, telephone poles), and type of road (rural segments). Additionally, the collision location (left side), gender of the driver (female), alcohol use, collision facility (street trees, traffic signals, telephone poles), and type of road (rural segments), in order of influence, were found to be the factors affecting the crash severity in accidents due to fixed roadside objects. CONCLUSIONS : An alternative solution is urgently required to reduce the crash severity in accidents due to fixed roadside objects. Such a solution can consider the appropriate places to install breakaway devices and energy-absorbing systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• 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.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.89-100
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• 2008

The purpose of this paper is to analyze the differences between the severity of punishments for traffic laws related offenders and the choice of policy measures with a view to reducing traffic accidents. To this end, government data on the drivers given a sentence of license suspension or revocation were collected and analyzed in terms of sex, age, type of driver's license, driving career, number of traffic laws violations, and number of traffic accidents generated. The statistics of analysis of covariance and tests of significance were used and conducted to compare the effectiveness of punishment for the penalty and the incentive groups. The results showed that the incentive policy measure on the drivers who violate traffic regulations repeatedly is more effective to reduce the number of traffic offenders and traffic accidents.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.1-12
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• 2021

Emergence of the COVID 19 pandemic has resulted in a sharp increase in the number of two-wheeler vehicular traffic accidents, prompting the introduction of numerous efforts for their prevention. This study applied XGBoost to determine the factors that affect severity of two-wheeled vehicular traffic accidents, by examining data collected over the past 10 years and analyzing the influence of each factor. Among the total factors assessed, variables affecting the severity of traffic accidents were overwhelmingly high in cases of signal violations, followed by the age group of drivers (60s or older), factors pertaining only to the car, and cases of centerline infringement. Based on the research results, a reasonable legal reform plan was proposed to prevent serious traffic accidents and strengthen safety management of two-wheeled vehicles. Based on the research results, we propose a reasonable legal reform plan to prevent serious traffic accidents and strengthen safety management of two-wheeled vehicles.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2483-2491
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• 2013

In order to identify and understand the crucial factors to induce traffic accident, causal relationships between diverse factors and traffic accident occurrence have been investigated continuously. It is one of most important issues all over the world to reduce the number of traffic accidents and deaths by them. Korea government is also stepping up their effort to reduce the number of traffic accidents and mitigate the severity of the accidents by establishing various traffic safety strategies. By introducing the five-day work week and increasing concern of leisure activities, the differences of trip characteristics between weekday and weekend is getting greater. According to this, the patterns and crucial factors of traffic accident occurrence in weekend appear differently from those in weekday. This study aims to understand major different factors affecting accident severity between weekday and weekend using 12,042 incident data occurred on freeways of Korea from 2006 to 2011. The model developed in this study estimated relationships among various exogenous factors of traffic accident by each type using SEM(Structural Equation Model). The result provides that road factors are related to the accident severity for weekday model, while environment factors affects on accident severity for weekend.

• International Journal of Highway Engineering
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• v.11 no.2
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• pp.195-203
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• 2009

This research wished to risk type and examine closely driver special quality and relation of traffic accidents by occurrence type of traffic accidents and traffic accidents seriousness examine closely relation with Severity. Fractionate traffic accidents type by eight, and driver's special quality for risk group's classification did to distinction of sex, vehicle type, age etc. analyzed relation with injury degree adding belt used putting on availability for security the objectivity with wave. Used log-Linear model and Logit model for analysis of category data. A head-on collision and overtaking accident, right-turn accident are high injury or death accident and possibility to associate in relation with accident type and seriousness degree. In risk group analysis The age less than 20 years in motor-cycle driver, taxi driver in 41 years to 50 years old are very dangerous. The woman also was construed to the more risk group than man from when related to car, mini-bus, goods vehicle etc. Therefore, traffic safety education and Enforcement for risk group that way that can reduce accident that produce to reduce a loss of lives at traffic accidents appearance a head-on collision and overtaking accidents, right-turn accidents should be studied and as traffic accidents weakness class may have to be solidified.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.1-12
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• 2017

Traffic accidents are caused by a combination of human factors, vehicle factors, and environmental factors. In the case of traffic accidents where rental cars are involved, the possibility and the severity of traffic accidents are expected to be different from those of other traffic accidents due to the unfamiliar environment of the driver. In this study, we developed a model to forecast the severity of rental car accidents by using Naive Bayes classifier for Busan, Gangneung, and Jeju city. In addition, we compared the prediction accuracy performance of two models where one model uses the variables of which statistical significance were verified in a prior study and another model uses the entire available variables. As a result of the comparison, it is shown that the prediction accuracy is higher when using the variables with statistical significance.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.40-46
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• 2020

Modern traffic accidents are a complex occurrence. Various indicators are needed to analyze traffic accidents. Countries that have been investigating traffic accidents for a long time accumulate various data to analyze traffic accidents. The Korean In-Depth Accident Study (KIDAS) database collected damaged vehicles and severity of injury caused by Collision Deformation Classification code (CDC code), Abbreviated Injury Scale (AIS), and Injury Severity Score (ISS). As a result of the investigation, data relating to the injuries of the occupants can be easily obtained, but it was difficult to analyze human severity based on the information of the damaged vehicle. This study suggests a method to measure the speed change at the time of an accident, which is one of the most important indicators in the vehicle crash database, to help advance KIDAS research.

• Journal of the Korean Society of Safety
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• v.32 no.6
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• pp.150-156
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• 2017

A frozen road surface increases traffic accidents during the winter season. Hence, information on easily-frozen road sections and their specificities are required to prevent traffic accidents. Frozen road surfaces are determined by equipment measuring road surface temperatures. However, there are limitations in investigating the entire road network. Therefore, it is imperative to develop new methods that effectively determine road surface freezing risks. Meteorologically, road surfaces are frozen when the actual temperature cools down to the dew point temperature. Under this condition, there is likely to be frost if relative humidity reaches 100% and frozen road surfaces as the temperature gets lower. Meteorological characteristics give us an alternative to a direct measurement road surface temperature to estimate risks of road surface freezing. Based on the clues, the relationship between severity of traffic accidents and temperature changes is empirically investigated using Paju weather data. The results reveal that as the temperature gets lower and changes in current temperature are relatively small, the severity of traffic accidents become higher. In addition, the same is true when the difference between current temperature and the dew point temperature is relatively small, as it increases possibilities of road surface freezing. Future studies must investigate how current temperature and the dew point temperature affect road surface freezing and thereby establish a time-space scope to estimate possible road surface freezing sections using only weather and road material type data. This would provide invaluable information for predicting and preventing frozen road accidents based on weather patterns.