• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.917-923
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• 2018

In recent, technological advancement including a vehicle black box (VBB) has led to reducing such underreporting issues and errors of crash data. The objective of this study is to analyze the injury severity of cyclists on taxi-to-two wheeler crashes based on the accurate crash data collected from the VBB in taxi. This study defined the two wheelers as bicycle and motorcycle. To perform this study, we used the VBB data collected from taxis operating in Incheon, South Korea for a two-year period (2010-2011). An ordered probit model was applied to analyze the injury severity in crashes. As a result, new injury severity factors were found: increase of the crash speed of taxi, damage of crash-involved vehicles (i.e., taxi and/or two wheeler), not standing of cyclists after crash, and second or third impact of cyclists after first crash.

• Journal of Korean Society of Transportation
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• v.34 no.2
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• pp.123-134
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• 2016

Traffic accident at intersections takes 44.3% of total number of accidents on entire road network of Korea in 2014. Although several studies addressed contributory factors of accidents at signalized intersection, very few is known about the factors at rural unsignalized intersections. The objective of this study is therefore to investigate specific characteristics of crashes at rural unsignalized intersection and to identify contributory factors in causing crashes by statistical approach using the Ordered Logistic Regression Model. The results show that main type of car crashes at unsignalized intersection during the daytime is T-bone crashes and the number of crashes at 4-legged intersections are 1.53 times more than that at 3-legged intersections. Most collisions are caused by negligence of drivers and violation of Right of Way. Based upon the analysis, accident severity is modeled as classified by two types such as 3-legged intersection and 4-legged intersection. It shows that contributory factors in causing crashes at rural unsignalized intersections are poor sight distance problem, average daily traffic, time of day(night, or day), angle of intersection, ratio of heavy vehicles, number of traffic violations at intersection, and number of lanes on minor street.

• Journal of Korean Society of Transportation
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• v.29 no.3
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• pp.103-114
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• 2011

In this study, we discuss a fatal accident severity model obtained from the analysis of 112 crash sites collected since 2000, and the resulting relationship between fatal accidents and roadway geometry design. From the 720 times computer simulations for improving driving safety, we then reached the following conclusions:. First, the result of cross and frequency-analyses on the car accident sites showed that 43.7% of the accidents occurred on the curved roads, 60.7% on the vertical curve section, 57.2% on the roadways with radius of curvature of 0 to 24m, 83.9% on the roads with superelevation of 0.1 to 2.0% and 49.1% on the one-way 2-lane roads; vehicle types involved are passenger vehicles (33.0%), trucks (20.5%) and buses (14.3%) in order of frequency. The results also show that the superelevation is the most influencing factor for the fatal accidents. Second, employing the Ordered Probit Model (OPM), we developed a severity model for fatal accidents being a function of on various road conditions so as to the damages can be predicted. The proposed model possibly assists the practitioners to predict dangerous roadway segments, and to take appropriate measures in advance. Third, computer simulation runs show that providing adequate superelevation on the segment where a fatal accident occurred could reduce similar fatal accidents by at least 85%. This result indicates that the regulations specified in the Rule for Road Structure and Facility Standard (description and guidelines) should be enhanced to include more specific requirement for providing the superelevation.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.173-184
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• 2008

Development of an accident estimation model based on accident data can be made after accident occurrences. However, the taking of historical accident data is not easy, and there have been differences between real accident data and police-reported accident data. Also, another difficult shortcoming is that historical traffic accident data better consider driver behavior or intersection characteristics. A new method needs to be developed that can predict accident occurrences for traffic safety improvement in black spots. Traffic conflict decision techniques can acquire and analyze data in time and space, requiring less data collection through investigation. However, there are shortcomings: as existing traffic conflict techniques do not operate automatically, the analyst's opinion could easily affect the study results. Also, existing methods do not consider the severity of traffic conflicts. In this study, the authors presented traffic conflict decision criteria which consider conflict severity, including opposing left turn traffic conflict and cross traffic conflict decision criteria. In order to test these criteria, the authors acquired three signalized intersection images (two intersections in Sungnam city and one intersection in Paju) and analyzed the acquired images using image processing techniques based on individual vehicle tracking technology. Within the analyzed images, level 1 conflicts occurred 343 times over three intersections. Some of these traffic conflicts resulted in level 3 conflict situations. Level 3 traffic conflicts occurred 25 times. From the study results, the authors found that traffic conflict decision techniques can be an alternative to evaluate traffic safety in black spots.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.90-102
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• 2022

Currently, in our society with a substantial and increasing fraction of the elderly population, transport safety for elderly drivers is becoming the center of attention. However, deficient data on vehicle crashes in South Korea limits the growth of traffic accident research pertaining to the country. So, we complemented South Korean vehicle crash data by examining USA vehicle crash data, especially the data of Ohio State, and analyzing the influential factors of elderly driver-involved crashes of the State. Subsequently, we suggested a way of improving the South Korean dataset. Notably, our study showed that the influential factors were vehicle speed, posted speed, and following other vehicles too close and provided them in the South Korean dataset.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.114-131
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• 2023

Personal Mobility (PM)-related accidents have increased rapidly since PM use was activated. In response to the increase in these accidents, the government strengthened regulations for PM users on May 13, 2021. The number of the accidents in which the PM user was a victim decreased significantly. In contrast, the increasing number of accidents in which PM user was the offender did not decrease significantly. In most of these accidents, the PM user was the offender who crashed into pedestrians. Hence, the safety of pedestrians is threatened. Therefore, this study analyzed the factors, such as the regulations, urban built environment, and personal characteristics, affecting the severity of PM-pedestrian accidents by focusing on PM-pedestrian crashes. This study analyzed the PM-pedestrian accidents in Seoul from 2020 to 2021 using binary logistic regression model. Through these results, this study proposed the policy implications.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6D
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• pp.577-585
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• 2010

Traffic accidents are caused by several factors such as drivers, vehicles, and road environment. It is necessary to investigate and analyze them in advance to prevent similar and repetitive traffic accidents. Especially, the human factor is most significant element and traffic accidents by drinking-driving caused from human factor have become social problem to be paid attention to. The study analyzes traffic accidents resulting from drinking-driving and the effects of driver's attributes and environmental factors on them. The study is composed as two parts. First, the log-linear model is applied to analyze that accidents by drinking or non-drinking driving associate with road geometry, weather condition and personal characteristics. Probability is tested for drinking-driving accidents relative to non-drinking drive accidents. The study analyzes probability differences between genders, between ages, and between kinds of vehicles through odds multipliers. Second, traffic accidents related to drinking are classified into property damage, minor injury, heavy injury, and death according to their severity. Heavy injury is more serious than minor one and death is more serious than heavy injury. The ordinal regression models are established to find effecting factors on traffic accident severity.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.3
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• pp.1-17
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• 2018

Over the past five years (2010-2014), the total number of traffic accidents has decreased from 226,878 to 223,552 with decrease of 0.37 percent each year. The death toll has also decreased from 5,505 to 4,762. However, the number of rental car accidents and fatalities has been steadily increased. Despite of its growth, no previous study has been conducted on rental car accident severity. This study analyzed data of 18,050 rental car accidents in South Korea collected from 2010 to 2014 and then processed in order to identify which factors could affect the accident severity. Seventeen factors related to rental car accident severity were grouped into four categories: driver, vehicle, roadways and environment. As a result of the ordered probit model analysis, fourteen variables excluding age, intersection, and day of week were found to affect the severity of rental car accidents. The results of the study summarized as follows. First of all, violation of traffic regulations such as speeding increase the severity of rental car accidents. Secondly, rental accident severity is higher at curved sections of complicated roadway, which the driver's field of view is impaired. The results of this study can be used to reduce the severity of rental car accidents in transportation safety.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.3
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• pp.391-398
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• 2019

In general, truck-involved crashes increase severity in terms of both injury level and crash impact level. Recently, although the frequency and fatality of truck-involved crashes in Korea are rising, their associative studies are very limited. Therefore, the objective of this study is to identify critical factors influencing on injury severity of truck-involved crashes on Korean freeway system. To carry out this objective, this study uses an ordered probit model (OPM) based on a 6-year crash dataset from 2012 to 2017. From the analysis, eight variables were found to have a great effect on injury severity: older driver, crash speed, rear-end collision, number of vehicles involved, drowsy driving, nighttime (0:00 to 6:00) driving, overturn or rollover, and vehicle's fire after crash. However, injury severity was less severe in crashes under snowy condition and crashes to traffic facilities (i.e., crash alone).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.49-60
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• 2020

The number of traffic control equipment installed to prevent traffic accidents increases every year due to continuous installation by the National Police Agency and local governments. However, it is installed based on qualitative judgment rather than engineering analysis results. The purpose of this study was to present additional installations in the future by presenting the installation criteria considering the severity of accidents for each road type and calculating the appropriate number of installations. ARI indicators that can indicate the severity of traffic accidents were developed, and road types were classified through analysis of variance and cluster analysis, and accident information by road type was analyzed to derive ARI of clusters with high traffic accident severity. The ARI values required to determine the installation of equipment for each road type were presented, and 5,244 additional installation points were analyzed.