• 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.

• International Journal of Highway Engineering
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• v.10 no.2
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• pp.35-45
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• 2008

Trucking is the most frequently used mode for freight movement due to relatively lower shipping costs and its operational flexibility. However, truck traffic can contribute to serious safety problems where they occupy high percentage of the total traffic. Heavy truck crashes arc more likely to result in serious injuries and fatalities than are crashes involving light vehicles. Therefore, safety issues for truck traffic are very significant both for public agencies and for general travelers. The objective of our study is to find truck-involved accident patterns according to traffic conditions and main factors as well as to find the most critical factor through conventional statistical techniques. A vailable data were obtained from TASAS (Traffic Accident Surveillance and Analysis System). Once critical factors are identified, effective and efficient truck management strategies can be discussed.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.179-186
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• 2019

Identifying potential accidents at construction sites is a major concern for the construction industry, and, according to the Korea Occupational Safety and Health Agency, the death rate of safety accidents caused by construction equipment is particularly high at 19.8% as of 2016. Although Internet of Things (IoT) has not been applied widely in construction sites, it can build an operating system that feeds accurate and useful information to construction accident management for identifying potential accidents. In this context, this study proposes an IoT- and RTLS-based construction equipment safety accident prevention system, which can be useful for preventing and managing safety accidents caused by construction equipment. Future deployment of such system would contribute not only to the safety of workers but also to efficient equipment and manpower operation.

• Journal of the Korea Institute of Construction Safety
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• v.4 no.1
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• pp.9-15
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• 2021

Recently, buildings have become larger, more complex, and various construction methods have been tried. As a result, the use of construction equipment continues to increase, as well as safety accidents. According to the Ministry of Employment and Labor's report on industrial accidents, the rate of deaths caused by construction equipment among construction accidents has been increasing steadily since 2009. In the safety field of other industries such as crime and traffic, research has been continuously conducted to develop quantitative indicators due to demands for development of evaluation indicators or risk index development. On the other hand, construction equipment has been studied to analyze disaster cases and come up with improvement measures, but there is no research related to risk index. Therefore, the research will develop a quantitative index that can determine the risk level of construction equipment in the field based on the accident case and verify the possibility of use in the field.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.605-611
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• 2023

Accidents at construction sites have a very high rate of fatalities due to the nature of being prone to severe injury patients. In order to reduce the mortality rate of severely injury patients, quick response is required, and some systems that detect accidents using AI technology and cameras have been devised to respond quickly to accidents. However, since existing accident detection systems use only a single camera, there are blind spots, Thus, they cannot detect all accidents at a construction site. Therefore, in this paper, we present the system that minimizes the detection blind spot by using multiple cameras. Our implemented system extracts feature points from the images of multiple cameras with the YOLO-pose library, and inputs the extracted feature points to a Long Short Term Memory-based recurrent neural network in order to detect accidents. In our experimental result, we confirme that the proposed system shows high accuracy while minimizing detection blind spots by using multiple cameras.

• Journal of The Korean Association For Science Education
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• v.36 no.2
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• pp.347-360
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• 2016

This study focused on the use of student assessment activities to investigate the impact on reflective thinking in Argument-based Inquiry. The participants of the study were 166 10th grade students (six classes). Over one semester, students participated in five ABI programs that we developed. The experimental group (84 students) was taught Argument-Based Inquiry with students' self and peer assessment activities. The comparative group (82 students) was taught without the activities. We analyzed students' reflective writing to investigate how the student assessment activities influenced the students' reflective thinking. We also used the interviews and surveys to examine the validity of student assessment activities. According to analysis of the reflective writing, the experimental group had a significantly higher mean score than the comparative group in the 3rd and 5th writing. The ratio of students who showed a metacognitive level of reflection with regard to analysis of inquiry process, understanding of learning, and change of thinking increased in both groups, but the experimental group's ratio was higher than the comparative group's. The result of analysis of the reflective practice showed that the ratio of the experimental group's students who reached the metacognitive level of reflection in their writing increased, while the comparative group's decreased. Therefore, we conclude that student assessment activities can create a learning environment that facilitates student participation, increases the students' engagement in the learning process, and can be used as a tool to scaffold learning.

• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.105-115
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• 2017

Each year, a number of tourists visit Jeju Island, a popular tourist destination in the Republic of Korea. A large portion of the tourists (about 61%) use a rental car as a means of transportation. With this reason, the number of rental cars registered in Jeju was 15,517 in 2011, while the total number of the rental car has rapidly increased to 26,338 in 2015. For the same period, the number of rental car involved traffic accidents has been doubled. Thus, this study aims to analyze the rental car accidents' characteristics, clarifying primary factors related to rental car accidents in Jeju Island. To do this, 918 rental car accidents and 4,201 non-commercial passenger car accidents that occurred in Jeju island over the two years (2014-2015) were compared, using statistical methods such as chi-square test and z-test. The results show that the characteristics of rental car involved accidents are different from those caused by the passenger cars. Most of the rental car accidents in Jeju were caused by young drivers and drivers who had just obtained their driver's licenses. This study finds that driver immaturity, unfamiliar geography, and driving an unfamiliar vehicle are the main causes of the rental car accidents. Statistical analysis confirms that the characteristics of these accidents appeared significantly different from the passenger cars in terms of human and environmental factors. On the other hand, there is no clear evidence that vehicle-related characteristics are different between rental car and non-commercial passenger car accidents. The implications on transportation safety analysis and effective solutions to prevent rental car traffic accidents are discussed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.132-145
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• 2022

This study was based on the black box images of traffic accidents on highways, cluster analysis and prediction model comparisons were carried out. As analysis data, vehicle driving behavior and road surface conditions that can grasp road and traffic conditions just before the accident were used as explanatory variables. Considering that traffic accident data is affected by many factors, cluster analysis reflecting data heterogeneity is used. Each cluster classified by cluster analysis was divided based on the ratio of the severity level of the accident, and then an accident prediction evaluation was performed. As a result of applying the Logit model, the accident prediction model showed excellent predictive ability when classifying groups by cluster analysis and predicting them rather than analyzing the entire data. It is judged that it is more effective to predict accidents by reflecting the characteristics of accidents by group and the severity of accidents. In addition, it was found that a collision accident during stopping such as a secondary accident and a side collision accident during lane change act as important driving behavior variables.

• 한국노년학
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• v.30 no.3
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• pp.843-853
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• 2010

The purpose of this study is to find out the characteristics of elderly pedestrian accidents and to suggest policy implications to enhance the level of elderly pedestrian safety. Although much efforts has made to enhance traffic safety environment, pedestrian traffic accidents among elderly population are not significantly decreased. This is mainly because current traffic safety measures do not much consider the characteristics of elderly pedestrians in the aspects of physical and psychological conditions. Main findings from vehicle-pedestrian traffic accident data and survey are as follows. First elderly pedestrians have high probability of traffic accident near crosswalks or cross streets rather than on crosswalk or cross streets. Second they need more green light time for crossing the streets. Third, they feel motor cycles running on the side walk and parked vehicles on the side walk are the most dangerous factors. Forth, general drivers do not have reasonable understanding for the walking behaviors of elderly pedestrians. Fifth, elderly pedestrians frequently need to rest while walking. Sixth, elderly people do not see clearly or understand traffic signs. Finally, many elderly pedestrians experience accidents or inconvenience while walking on the sidewalk.

• Journal of Navigation and Port Research
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• v.39 no.3
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• pp.149-156
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• 2015

The Dynamic Positioning System consists of 7 elements which are namely Power system, Human machine interface, DP Computer, Position Reference System(PRS), Sensors, Thruster system and DP Operator. Incidents like loss of position(LOP) on DP vessel usually occur due to errors in these 7 elements. The purpose of this study is to find out safety operation method of DP vessel through qualitative and quantitative analyze of DP LOP incidents which are submitted to IMCA every year. The 612 DP LOP incidents submitted from 2001 to 2010 were analyzed to find out the main cause of the incidents and its rate among other causes. Consequently, the highest rate of incidents involving DP elements are PRS errors. DP computer, Power system, Human error and thruster system came next. The PRS has been analyzed and a flowchart was drawn through expert brainstorming. Also, the conditional probability has been analyzed through Bayesian Networks based on this flowchart. Consequentially, the main causes of drive off incidents were DGPS, microwave radar and HPR. Also, this study identified the main causes of DGPS errors through Bayesian Networks. These causes are signal blocked, electric components failure, relative mode error, signal weak or fail.