• 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 Digital Convergence
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• v.18 no.10
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• pp.33-39
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• 2020

In order to cope with traffic accidents efficiently, the maximum number of traffic accidents, deaths and serious injuries that can occur during the day should be presented quantitatively. In order to examine the characteristics of traffic accidents in different regions, it was divided into the Seoul metropolitan area, Chungcheong area, Gyeongbuk area, Honam area, and Gyeongnam area and was suitable for the generalized extreme value distribution (GEV). The parameters of the GEV distribution were estimated by the L-moments, and the Anderson-Darling test and the Cramer-von Mises test confirmed the suitability of the distribution. According to the analysis, the maximum number of traffic accidents that can occur once every 50 years is 401 in the Seoul metropolitan area, 168 in the South Gyeongsang region, 455 in the North Gyeongsang region, 136 in the Chungcheong region and 205 in the South Jeolla region. Compared to the Seoul metropolitan area, which has a large population and car registration, the number of traffic accidents is relatively high due to the large area, mountainous areas, and logistics movement caused by the industrial complex.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.13 no.1
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• pp.63-68
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• 1991

The author has arrived at the following result after having carried out multilateral study based on a total of 282 maxillofacial fracture patients who have receive treatment at the Euijeongbu general hospital and Shinchun general hospital in the northern district of Kyunggido from march 1988 to august 1990. 1. Sex distribution of Mx. facial fx. patient was higher in male by 4.6:1 and was predominant in the 3rd decade with 40.4% followed in decreasing order by the 2nd decade and the 4th. 2. A majority were in the Mn. with 40.2% followed in decreasing order by zygoma. nasal bone and maxilla. 3. For the sex distribution according to anatomy, make to female ratio was 6.2:1 in the mandible, followed in decreasing order by zygoma, and nasal bone with predominance in male. 4. Car accident with 42.8% was the most common cause of fx. followed in decreasing order by violence, workmen's accident, and fall down. 5. The involvement of other trauma areas are head. 79.0%, abdomen-thorax, and the extremities in decreasing order. 6. In the mandibular fx. a majority were in the symphysis with 73.9% followed in decreasing order by angle, Condyle, and body. 7. Maxillary fx. of the type LeFort II was estimated to be 41.2% 8. Fracture in the zygoma including zygomatic arch was estimated to be 72.5%

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.202-217
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• 2021

Following the fatal accident of pedestrians caused by Autonomous Vehicle by Uber, the world's largest ride-hailing company, two people were killed in a self-driving car accident by Tesla in April. There is a need to ensure the safety of road users. Accordingly, in order to secure the safety of Autonomous Vehicle driving, it is necessary to evaluate Autonomous Vehicle driving technologies in various situations based on the road and traffic environment in which the Autonomous vehicle will actually drive. Therefore, this study used UC-win/Road ver.14.0 based on general driver's license test questions to present a virtual reality-based Autonomous Vehicles driving ability evaluation tool among various driving ability test method. Based on this, it was intended to test driving ability for unexpected situations in complex and diverse driving environments, and to confirm its practical applicability as an optimal tool for Autonomous vehicle ability test and evaluation.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.305-316
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• 2022

Hydrogen energy is emerging as an alternative to the depletion of fossil fuels and environmental problems, and the use of hydrogen vehicles is increasing in the automobile industry as well. However, since hydrogen has a wide flammability limit of 4 to 75%, there is a high concern about safety in case of a hydrogen car accident. In particular, in semi-enclosed spaces such as tunnels and underground parking lots, a fire or explosion accompanied by hydrogen leakage is highly likely to cause a major accident. Therefore, it is necessary to review hydrogen safety through analysis of flammability areas caused by hydrogen leakage. Therefore, in this study, the effect of the air velocity in the tunnel on the flammability area was investigated by analyzing the hydrogen concentration according to the hydrogen leakage conditions of hydrogen vehicles and the air velocity in the tunnel in a road tunnel with standard section. Hydrogen leakage conditions were set as one tank leaking and three tanks leaking through the TPRD at the same time and a condition in which a large crack occurred and leaked. And the air velocity in the tunnel were considered 0, 1, 2.5, and 4.0 m/s. As a result of the analysis of the flammability area, it is shown that when the air velocity of 1 m/s or more exists, it is reduced by up to 25% compared to the case of air velocity of 0 m/s. But there is little effect of reducing the flammability area according to the increase of the wind speed. In particular, when a large crack occurs and completely leaks in about 2.5 seconds, the flammability area slightly increases as the air velocity increases. It was found that in the case of downward ejection, hydrogen gas remains under the vehicle for a considerably long time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.2A
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• pp.203-212
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• 2007

An advanced vehicle safety system can be constructed by exchanging danger-related information ,such as urgency stop, traffic accident, obstacle, and car trouble, among the vehicles. However, because network topology changes rapidly and frequently due to the mobility of vehicles, it is impossible to configure the network for information forwarding in this environment. In the most of vehicle safety communication applications, an emergency message is propagated in a form of broadcasting. The simple broadcasting causes a lot of problems in terms of efficiency due to multi-hop area and radio collision problem. This paper proposes a method of selective message forwarding with stem and branch structure for propagating the emergency messages. However, the proposed method raise the efficiency of message transmission with the selective forwarding based on the priority assignment as its location. We analyze and evaluate the performance by comparing the proposed scheme with other schemes that are presented in the paper.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.837-839
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• 2012

When the for occurred, the driver does not get the vision is has difficult on driving. In this case, the probability of occurrence of accidents are very high level. To reduce accidents, this system provide drivers with the safety of ensure to measures that a service inform current situation. in this paper, the crash occur in fog to prevent accident using vehicle safety system to give a alarm and control. The proposed system is installed on the outside of the vehicle, humidity, and ambient light sensors inside the car from the information collected by the system controller for the detection of fog conditions using video equipment and then finally the fog occurs if you do not get the driver's field of events is causing the system.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.295-300
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• 2012

Various forward collision warning algorithms have studied in order to protect a car accident. For this, in general, algorithms using an external device such as a camera and sensor generate a forward collision warning. However, if using the external device, it can occur errors due to device characteristics when there is rain or fog. Also, the prevention of a chain-reaction collision is insufficient because the system generates a warning in case of only vehicle having a forward collision danger. If it combines the vehicle safety communications, the method becomes a solution to protect a chain-reaction collision. So, In this paper, we proposes a improved forward collision warning algorithm using the wireless communication technique, driver's information, breaking distance, and velocity. And we compare and analyze our algorithm and previous algorithms.

• 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.