• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.423-438
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• 2021

The blade of motor grader is used for scattering and leveling the aggregates on the foundation of road construction site. The paper performed a design improvement research of the blade part to enhance the working efficiency of motor graders. The scattering works of aggregates by blade driving were simulated by DEM (discrete element method) of a dynamic code. The four design parameters were selected and a specific leveling scenario for the simulation was determined. The nine blade models were numerically experimented, and the sensitivity of each factors was analyzed. Next, the design factors that influence a blade performance have been selected by ANOVA, and these key design factors were applied to the progressive quadratic response surface method (PQRSM). The optimum set of design factors of the blade was finally proposed.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.69-75
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• 2022

In order to prevent traffic accidents for children who are vulnerable to traffic around elementary schools, a school zone in the children's protection zone is designated, and parking and stopping are prohibited in this area as the vehicle speed is less than 30km/h. However, Korea has a disgrace that the death rate of children from traffic accidents is the No. 1 among OECD countries. Against this backdrop, this study aims to contribute to preventing traffic accidents and raising awareness of driver safety by establishing an illegal parking and stopping system in the child protection zone due to various road conditions in the child protection zone. As a research method, a plan to build a parking and stopping prevention system was presented based on major preceding studies and literature investigation and analysis. Through the construction plan, effects such as preventing traffic accidents, inducing smart drivers to drive safely, strengthening pedestrian safety awareness, and inducing driver's awareness of safety can be expected.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.250-265
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• 2022

Positive effects such as significantly reducing traffic accidents caused by human error can be expected by the introduction of Automated vehicles (AV). However, as new traffic safety issues are expected to occur in the future due to errors in H/W or S/W of autonomous vehicles and lack of its function, it is necessary to establish a scenario to evaluate the driving safety of AV. Therefore, in this study, functional scenario was developed to evaluate the driving safety of AV based on traffic accident data of the National Police Agency. Using the GIS program, QGIS, traffic accident data that occurred in the toll gate and ramp sections of expressway were extracted and accident summary items were checked to classify the types of accident. In addition, based on the results of accident type classification, functional scenario were developed that contains various dangerous situations in the tollgate and ramp sections.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.133-146
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• 2023

Traffic states in urban areas are essential to implement effective traffic operation and traffic control. However, installing traffic sensors on numerous road sections is extremely expensive. Accordingly, estimating the traffic state using a vehicle-mounted camera, which shows a high penetration rate, is a more effective solution. However, the previously proposed methodology using object tracking or optical flow has a high computational cost and requires consecutive frames to obtain traffic states. Accordingly, we propose a method to detect vehicles and lanes by object detection networks and set the region between lanes as a region of interest to estimate the traffic density of the corresponding area. The proposed method only uses less computationally expensive object detection models and can estimate traffic states from sampled frames rather than consecutive frames. In addition, the traffic density estimation accuracy was over 90% on the black box videos collected from two buses having different characteristics.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.78-96
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• 2023

Urban traffic information is collected by various systems such as VDS, DSRC, and radar. Recently, with the development of deep learning technology, smart intersection systems are expanding, are more widely distributed, and it is possible to collect a variety of information such as traffic volume, and vehicle type and speed. However, as a result of reviewing related literature, the performance evaluation criteria so far are rbs-based evaluation systems that do not consider the deep learning area, and only consider the percent error of 'reference value-measured value'. Therefore, a new performance evaluation method is needed. Therefore, in this study, individual error, interval error, and overall error are calculated by using a formula that considers deep learning performance indicators such as precision and recall based on data ratio and weight. As a result, error rates for measurement value 1 were 3.99 and 3.54, and rates for measurement value 2 were 5.34 and 5.07.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.29 no.2
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• pp.129-138
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• 2023

The proportion of agricultural products handled through the Agricultural Products Processing Center (APC) is also steadily increasing every year, and in the case of Seongju Korean melon, a total of 10 APCs of Nonghyup and farming association corporations are in operation, and the distribution ratio is about 60% based on total production. In this study, Seongju Korean melon was selected as a target to analyze the environment load during carrying (production farm ~ APC) in the production area and the transport environment load during distribution of domestic fruits, and to analyze the environmental load for handling at APC. The vertical average vibration intensity (overall G_rms of 1~250 Hz) of truck transport measured at three transport routes from Seongju Korean melon producer ~ APC, Seongju ~ Seoul and Seongju ~ Jeju was about three times larger than that in the lateral direction and 4.5 times larger than that in the longitudinal direction, respectively. The frequency of occurrence of high-amplitude events (G) in the vertical direction compared to the measuring time was deeply related to pavement conditions in the order of unpaved farm-roads, concretepaved farm-roads, and asphalt-paved main-roads, but overall G_rms for the entire frequency band is believed to have a greater impact on vehicle traveling speed than road conditions. On the other hand, the difference in the size and direction of the vibration intensity measured by the forklift truck's main-body and the attachment (fork carrier) during handling at Seongju Korean melon APC was clear, and the vibration intensity of the forklift truck's main-body was largely affected by the stiffness of the fork and the mast according to the handling weight. Based on the field-data of the transport environment during domestic distribution measured through this study, it is believed that it is possible to develop a lab-based simulation protocol for appropriate packaging design.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.485-496
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• 2010

An increase in number of vehicles owned by individuals worsens the conditions of parking in residential areas. So to speak, the pedestrians' safety is seriously threatened by illegally parked vehicles on the residential area roads. As a result, the number of vehicle-to-pedestrian accidents has been increasing annually. Also, illegal parking on arterial roads is increasingly becoming common, especially by cooommercial trucks. However, no solution has been found to reduce or eliminate accidents caused by vehicles illegally parked on readside. The result of this study provides the solutions to enhance the safety of residential area roads and arterial roads, from viewpoints of both long-term and short-term, by collecting data about the status of illegal parking and addressing critical problems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.59-64
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• 2024

Recently, research on the convergence of drones and artificial intelligence technologies have been conducted in various industrial fields. In this paper, we propose an illegal parking vehicle recognition model using deep learning-based object recognition and classification algorithms. The model of object recognition and classification consist of YOLOv8 and ResNet18, respectively. The proposed model was trained using image data collected in general road environment, and the trained model showed high accuracy in determining illegal parking. From simulation results, it was confirmed that the proposed model has generalization performance to identify illegal parking vehicles from various images.

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.233-254
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• 2017

The purpose of this study is to analyze the main factors influencing the severity of traffic accidents and to visualize spatiotemporal characteristics of traffic accidents in Seoul. To do this, we collected the traffic accident data that occurred in Seoul for four years from 2012 to 2015, and classified as slight, serious, and death traffic accidents according to the severity of traffic accidents. The analysis of spatiotemporal characteristics of traffic accidents was performed by kernel density analysis, hotspot analysis, space time cube analysis, and Emerging HotSpot Analysis. The factors affecting the severity of traffic accidents were analyzed using decision tree model. The results show that traffic accidents in Seoul are more frequent in suburbs than in central areas. Especially, traffic accidents concentrated in some commercial and entertainment areas in Seocho and Gangnam, and the traffic accidents were more and more intense over time. In the case of death traffic accidents, there were statistically significant hotspot areas in Yeongdeungpo-gu, Guro-gu, Jongno-gu, Jung-gu and Seongbuk. However, hotspots of death traffic accidents by time zone resulted in different patterns. In terms of traffic accident severity, the type of accident is the most important factor. The type of the road, the type of the vehicle, the time of the traffic accident, and the type of the violation of the regulations were ranked in order of importance. Regarding decision rules that cause serious traffic accidents, in case of van or truck, there is a high probability that a serious traffic accident will occur at a place where the width of the road is wide and the vehicle speed is high. In case of bicycle, car, motorcycle or the others there is a high probability that a serious traffic accident will occur under the same circumstances in the dawn time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.679-690
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• 2015

The need to remove the cause of traffic accidents by improving the engineering system for a vehicle and the road in order to minimize the accident hazard. This is likely to cause traffic accident continue to take a large and significant social cost and time to improve the reliability and efficiency of this generally poor road, thereby generating a lot of damage to the national traffic accident caused by improper environmental factors. In order to minimize damage from traffic accidents, the cause of accidents must be eliminated through technological improvements of vehicles and road systems. Generally, it is highly probable that traffic accident occurs more often on roads that lack safety measures, and can only be improved with tremendous time and costs. In particular, traffic accidents at intersections are on the rise due to inappropriate environmental factors, and are causing great losses for the nation as a whole. This study aims to present safety countermeasures against the cause of accidents by developing an intersection Traffic safety evaluation model. It will also diagnose vulnerable traffic points through BPA (Back -propagation algorithm) among artificial neural networks recently investigated in the area of artificial intelligence. Furthermore, it aims to pursue a more efficient traffic safety improvement project in terms of operating signalized intersections and establishing traffic safety policies. As a result of conducting this study, the mean square error approximate between the predicted values and actual measured values of traffic accidents derived from the BPA is estimated to be 3.89. It appeared that the BPA appeared to have excellent traffic safety evaluating abilities compared to the multiple regression model. In other words, The BPA can be effectively utilized in diagnosing and practical establishing transportation policy in the safety of actual signalized intersections.