• International journal of advanced smart convergence
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• v.9 no.4
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• pp.1-7
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• 2020

This study is a study on the development of AEBS test scenarios for traffic accidents in Korea, and was compared and analyzed using the Traffic Accident Analysis Program. To ensure the safety of passengers and pedestrians in traffic accidents, the number of cars equipped with ADAS is increasing rapidly at all car manufacturers in each country. For traffic accidents used in this study, the domestic traffic accident database (ACCC) produced by SAMSONG was used. Domestic traffic accidents differ from overseas traffic accidents in terms of road type, signal system, driver's seat location and number of vehicles. ACCC databases, which supplemented and reinforced these differences, built a database based on the PC-CRASH program. In the study, we analyze the types of accidents to develop comparative scenarios for each type of road and collision type of traffic accidents. When the road types of traffic accidents in Korea were divided into five types and the collision types were divided into six, it was confirmed that the most types of FRONT-SIDE crashes appeared at the intersection. It is expected that the frequency of possible traffic accidents and collision types can be predicted according to the road type in the accident database, we that it can be used as an AEBS test scenario development suitable for the domestic road environment.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.91-100
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• 2010

Existing traffic safety studies have focused on identifying the relationship among roadway crashes, highway design and incremental weather condition such as rainy/ice weather. However, it is hard to find researches that studied the effect of sun glare on traffic safety although there are abundant evidences demonstrating that fatal traffic crashes are attributed to the sun glare. Affecting drivers'vision particularly during the morning or the evening time when the sun positions close to the horizon, sun glare directly deteriorate drivers'judgmental capability. In this paper, we numerically analyze the effect of sun glare on the drivers'vision in time and space domains. Applied to the roadways around St Louis area in the United States, the GIS based simulation analysis identifies the time of day in a year and the segments of highways that are potentially influenced by the sun glare. This study evidentially confirms the fact that roadway bounded for West and East directions have longer time influenced by sun glare particularly during Spring and Fall season than other roadways. The computational result provides risky time periods of day at intersections or pedestrian crossings where the sun glare potentially endangers traffic safety, which be utilized to reduce the crashes due to the sun glare.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.127-138
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• 2008

Vehicles have recently become one of the main factors affecting our quality of life, and the needs of vehicles are still increasing. As a result, the growth of vehicles generate more crashes every year. One main factor for vehicle crashes is uncareful driving behaviors. Especially, drowsy or fatigue driving behaviors explain about 10-20% of the crashes, and they cause serious results because of the delay of response time and the decrease of object-recognition. Therefore, this research conducted real time image processing tests in order to study how cellular phone usages and drowy(or fatigue) drives affect driving behaviors. A vehicle simulator was used for this research, and the faceLAB 4.5 of Seeing Machines for eye image tracking tests using a small camera was installed in the front of the simulator, and normal and drowsy(or fatigue) driving patterns were analyzed.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.39-46
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• 2022

As the link between traffic accidents and social and economic losses has been confirmed, there is a growing interest in developing safety policies based on crash data and a need for countermeasures to reduce severe crash outcomes such as severe injuries and fatalities. In this study, we select Daejeon city where the relative proportion of fatal crashes is high, as a case study region and focus on the severity of pedestrian crashes. After a series of data manipulation process, we run machine learning algorithms for the optimal model selection and variable identification. Of nine algorithms applied, AdaBoost and Random Forest (ensemble based ones) outperform others in terms of performance metrics. Based on the results, we identify major influential factors (i.e., the age of pedestrian as 70s or 20s, pedestrian crossing) on pedestrian crashes in Daejeon, and suggest them as measures for reducing severe outcomes.

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.68-80
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• 2016

Interest in freeway truck traffic has increased largely due to greater safety concerns regarding truck-related crashes. The negative interactions between slow-moving trucks and other vehicles are a primary cause of hazardous conditions, which lead to crashes with larger speed variations. To improve operational efficiency and safety, providing a climbing lane that separates slow-moving trucks from higher performance vehicles is frequently considered when upgrading geometrics. This study developed an operations strategy for freeway climbing lanes based on traffic conditions in real time. To consider traffic safety when designing a dynamic strategy to determine whether a climbing lane is closed or open, various factors, including the level of service (LOS) and the percentage of trucks, are investigated through microscopic simulations. A microscopic traffic simulator, VISSIM, was used to simulate freeway traffic streams and collect vehicle-maneuvering data. Additionally, an external application program interface, VISSIM's COM-interface, was used to implement the proposed climbing lane operations strategies. Surrogate safety measures (SSM), including the frequency of rear-end conflicts and, were used to quantitatively evaluate the traffic safety using an analysis of individual vehicle trajectories obtained from VISSIM simulations with various operations scenarios. It is expected that the proposed algorithm can be the backbone for operating the climbing lane in real time for safer traffic management.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.161-171
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• 2023

It is largely agreed that elderly drivers (over 64 years) are more likely to cause fatal crashes than other age groups. According to national road crash statistics 2021, the number of road fatalities per 10,000 drivers over 64 years old was 1.77, while that of drivers in their 30s was 0.55. This indicates a 2.67 times higher probability of causing crashes among the former than the latter. The current study estimates how rear-end crashes may be reduced by installing Automatic Emergency Braking Systems (AEBS), particularly for elderly drivers. We analyzed data from Samsung Fire & Marine Insurance. The results show that the Odds Ratio of rear-end crash occurrence between vehicles with AEBS and without AEBS is 0.75, implying there were lesser rear-end crashes in the vehicles installed with AEBS. The Odds Ratio of male drivers was determined to be 0.78, which was lesser than the 0.81 Odds Ratio obtained for female drivers. Elderly drivers who had installed AEBS in their vehicles showed an Odds Ratio of 0.76, implying crash reduction. In particular, the Odds Ratio of male elderly drivers was found to be the lowest at 0.49. We believe incentivizing by giving discounted insurance premiums to the elderly who drive vehicles installed with AEBS will help reduce rear-end crashes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.1017-1023
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• 2017

Duration induced by freeway crashes has a critical influence on traffic congestion. In general, crash duration composes detection and verification, response, and clearance time. Of these, the crash clearance time determined by a crash clearance team has attracted considerable attention in the freeway congestion management since the interest of the first two time stages faded away with increasing ubiquitous mobile phone users. The objective of this study is to identify the critical factors that affect freeway crash clearance time using a Cox's proportional hazard model. In total, 6,870 crash duration data collected from 30 major Korean freeways in 2013 were used. As a result, it was found that crashes during the night, with trailer or larger size truck, and in tunnel section contribute to increasing clearance time. Crashes associated with fatality, completed damage of crashed vehicle (s), and vehicles' fire or rollover after crash also lead to increasing clearance time. Additionally, an increase in the number of vehicles involved resulted in longer clearance time. On the other hand, crashes in the vicinity of tollgate, by passenger car, during spring, on flat section, and of car-facility type had longer clearance time. On the basis of the results, this paper suggested some strategic plans and mitigation measures to reduce crash clearance time on Korean freeway systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1123-1130
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• 2020

This study aims to classify the severity in car crashes using five classification learning models. The dataset used in this study contains 21,013 vehicle crashes, obtained from Korea Expressway Corporation, between the year of 2015-2017 and the LightGBM(Light Gradient Boosting Model) performed well with the highest accuracy. LightGBM, the number of involved vehicles, type of accident, incident location, incident lane type, types of accidents, types of vehicles involved in accidents were shown as priority factors. Based on the results of this model, the establishment of a management strategy for response of highway traffic accident should be presented through a consistent prediction process of accident severity level. This study identifies applicability of Machine Learning Models for Predicting of the Severity of Car Traffic Accidents on a Highway and suggests that various machine learning techniques based on big data that can be used in the future.

• Journal of the Korean Society of Safety
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• v.34 no.5
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• pp.144-152
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• 2019

According to the National Police Agency, point-to-point speed enforcement system is being installed and operated in 97 sections across the country. It is more effective than other enforcement systems in terms of stabilizing the traffic flow and inhibiting the kangaroo effect. But it is only 5.1% of the total enforcement systems. The National Police Agency is also aware that its operation ratio is very low and it is necessary to expand point-to-point speed enforcement system. Hence, this study aims to provide the expansion basis of the point-to-point speed enforcement operation through analysis of the quantitative effects and development the accident prediction model. Firstly, this study analyzed the effectiveness of point-to-point speed enforcement system. Naive before-after study and comparison group method(C-G Method) were used as methodologies of analyzing the effectiveness. The result of using the naive before-after study was significant. Total accidents, EPDOs and casualty crashes decreased by 42.15%, 70.64% and 45.30% respectively. And average speed and the ratio of exceeding speed limit decreased by 6.92% and 20.50%p respectively. Moreover, using the C-G method total accidents, EPDOs and casualty crashes decreased by 31.35%, 66.62% and 10.04% respectively. And average speed and the ratio of exceeding speed limit decreased by 3.49% and 56.65%p respectively. Secondly, this study developed a prediction model for the probability of casualty crash. It was dependant on factors of traffic volume, ratio of exceeding speed limit, ratio of heavy vehicle, ratio of curve section, and presence of point-to-point speed enforcement. Finally, this study selected the most danger sections to the major highway and evaluated proper installation sections to the recent installation section by applying the accident prediction model. The results of this study are expected to be useful in establishing the installation standards for the point-to-point speed enforcement system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.33-43
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• 2022

The number of car crashes increases along with the increasing number of vehicles. Hence, diverse initiatives on traffic accidents have been implemented, targeting zero crash fatalities. According to the 3rd Traffic Safety Master Plan of 2016, the current standard selecting road accident black spots prioritizes locations with the high cumulative death toll. While this standard is suitable for roads that a city government manages to some extent, it is not suitable for roads less than 20 meters that a borough (Gu) handles. The roads under the supervision of a borough do not have enough death toll, and thus improvements on its road accident black spots are highly limited. In addition, discovering the causes of traffic accidents is not easy when the number of car accidents is obtained by considering only fatal accidents, which are relatively low in number. Therefore, including all traffic accidents might identify causes of accidents and result in better advancements. Therefore, this research follows rational decision-making and suggests new National Traffic Safety Master Plan standards. These new standards are obtained by comparing accident costs between the location of fatal crashes and road accident black spots. The analysis result shows that considering all types of accidents yields better results. For example, a Three-way Intersection in front of Zion Day Care Center, one of the selected spots under the current standard, has lower road crash costs than Sinchon Intersection, a selected spot under a new standard. Therefore, the study concludes that the standards to select road accident black spots need to include traffic accident severity and road crash costs.