• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.397-411
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• 2023

The number of deteriorated bridges with a service period of more than 30 years has been rapidly increasing in Korea. Accordingly, the importance of advanced maintenance technologies through the predictions of age-induced deterioration degree, condition, and performance of bridges is more and more noticed. The prediction method of the safety grade of bridges was proposed in this study using the classification models of the Decision Tree and the Random Forest based on machine learning. As a result of analyzing these models for the 8,850 bridges located in national roads with various evaluation indexes such as confusion matrix, balanced accuracy, recall, ROC curve, and AUC, the Random Forest largely showed better predictive performance than that of the Decision Tree. In particular, random under-sampling in the Random Forest showed higher predictive performance than that of other sampling techniques for the C and D grade bridges, with the recall of 83.4%, which need more attention to maintenance because of the significant deterioration degree. The proposed model can be usefully applied to rapidly identify the safety grade and to establish an efficient and economical maintenance plan of bridges that have not recently been inspected.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.160-174
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• 2023

LiDAR plays a key role in autonomous vehicles, and to improve its visibility, it is necessary to improve its performance and the detection objects. Accordingly, this study proposes a shape for traffic safety signs that is advantageous for self-driving vehicles to recognize. Improvement plans are also proposed using a shape-recognition algorithm based on point cloud data collected through LiDAR sensors. For the experiment, a DBSCAN-based road-sign recognition and classification algorithm, which is commonly used in point cloud research, was developed, and a 32ch LiDAR was used in an actual road environment to conduct recognition performance tests for 5 types of road signs. As a result of the study, it was possible to detect a smaller number of point clouds with a regular triangle or rectangular shape that has vertical asymmetry than a square or circle. The results showed a high classification accuracy of 83% or more. In addition, when the size of the square mark was enlarged by 1.5 times, it was possible to classify it as a square despite an increase in the measurement distance. These results are expected to be used to improve dedicated roads and traffic safety facilities for sensors in the future autonomous driving era and to develop new facilities.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6D
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• pp.745-752
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• 2009

Management of social infrastructure has been advanced from facility management (FM) to asset management (AM), which adopts the aggressive and proactive methods in predicting the deterioration of infrastructure, prevents failures, and eventually saves maintenance cost. Infrastructure asset management is not a simple engineering technique, but it is a new paradigm evolved from facility management practices. To implement the infrastructure asset management successfully, organizational reform is very important. This paper suggests critical success factors and key performance indicators to implement the infrastructure asset management for facility managers of government owned social infrastructures such as roads and bridges. Reorganizing the facility management group requires new vision, objectives, strategies for the paradigm-changing asset management. This paper uses Balanced Score Card (BSC) which is a proven method in measuring and setting new objectives for an organization. Once the performance indicators are reviewed repeatedly by facility managers through experts workshops, developed BSC can be used in practice. This paper discusses the development of robust BSC scoring method through in depth literature reviews and investigation of asset management practices of domestic and international cases.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1D
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• pp.79-87
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• 2010

The urban street has been escaped from functional and uniformed arrangement for merely movement to the space which can take the role of cozy and familiar culture. Therefore I tried to prepare a plan for the wide roads having streetscape which reveal area's identity as well as coziness and convenience by grasping the problem and its solution through analysing and deducing various elements with the subject of streetscape among urban landscape components, especially composing wide road's streetscape. First of all, this study made a simulation by deducing components of wide road's streetscape and controlling them with theoretical review of streetscape and consideration of wide road's local characteristic. With the evaluation on the base of them, that following four components could be the representative things of entrance part was confirmed by this study. They were familiar streetscape,symbolic scene as as an entrance gate, continuous and dynamic streetscape, and interesting and pleasant streetscape. And for the middle part, those four components could represent the wide road's streetscapeI were confirmed. They were familiar streetscape, safe and tender streetscape, pleasant and dynamic streetscape, and symbolic streetscape. It seems that the components of the streetscape obtained from the analysis of this study are utilized as reference data in the case of a future streetscape design conclusively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.759-766
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• 2008

Road classification system is the first step for determining the road function and design standards. Currently, roads are classified by various indices such as road location and function. In this study, we classify road using various traffic indices as well as to identify traffic characteristics for each type of road. To accomplish the objectives, mixture model was applied for classifying road and analyzing traffic characteristics using traffic data that observed at permanent traffic count stations. A total of 8 variables were applied: annual average daily traffic(AADT), $K_{30}$ coefficient, heavy vehicle proportion, day volume proportion, peak hour volume proportion, sunday coefficient, vacation coefficient, and coefficient of variation(COV). A total of 350 permanent traffic count points were categorized into three groups : Group I (Urban road), Group II (Rural road), and Group III (Recreational road). AADT were 30,000 for urban, 16,000 for rural, and 5,000 for recreational road. Group III was typical recreational road showing higher average daily traffic volume during Sunday and vacational periods. Group I showed AM peak and PM peak, while group II and group III did not show AM peak and PM peak.

• The Journal of Bigdata
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• v.7 no.1
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• pp.15-27
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• 2022

In the past five years, the fatality rate of single-vehicle accidents has been 4.7 times higher than that of all accidents, so it is necessary to establish a system that can detect and respond to single-vehicle accidents immediately. The IoT(Internet of Thing)-based real-time traffic accident recognition system proposed in this study is as following. By attaching an IoT sensor which detects the impact and vehicle ingress to the guardrail, when an impact occurs to the guardrail, the image of the accident site is analyzed through artificial intelligence technology and transmitted to a rescue organization to perform quick rescue operations to damage minimization. An IoT sensor module that recognizes vehicles entering the monitoring area and detects the impact of a guardrail and an AI-based object detection module based on vehicle image data learning were implemented. In addition, a monitoring and operation module that imanages sensor information and image data in integrate was also implemented. For the validation of the system, it was confirmed that the target values were all met by measuring the shock detection transmission speed, the object detection accuracy of vehicles and people, and the sensor failure detection accuracy. In the future, we plan to apply it to actual roads to verify the validity using real data and to commercialize it. This system will contribute to improving road safety.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.91-97
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• 2022

The purpose of this study was to analyze the measurements of blood pressure and time using manual and automatic blood pressure monitors in various road conditions to verify reliable blood pressure monitor in a moving ambulance. First, the manual blood pressure monitor palpation on unpaved roads showed a systolic pressure deviation of 5 mmHg. However, the automatic blood pressure monitor showed two measurement failures, one reading failure, and the measured systolic pressure deviation was 35 mmHg. The measurement time was 102 seconds faster on average than the automatic blood pressure monitor. Second, the palpation of the manual blood pressure monitor while going over speed bumps remained constant at 130 mmHg. However, the automatic blood pressure monitor had a systolic pressure deviation of 52 mmHg. The measurement time was 61 seconds faster on average than the automatic blood pressure monitor. Finally, the manual blood pressure monitor palpation on the sharp curve road showed a systolic pressure deviation of 5 mmHg. The automatic blood pressure monitor had one reading failure and the measured systolic pressure deviation was 21 mmHg. The measurement time showed that the manual blood pressure monitor was 101 seconds faster than the automatic blood pressure monitor. As a result, in a moving ambulance during an emergency, the manual blood pressure monitor showed high reliability because the blood pressure measurement was constant and the measurement time was short.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.288-304
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• 2022

Walking is an eco-friendly and sustainable means of transportation that promotes health and endurance. Despite the positive health benefits of walking, pedestrian safety is a serious problem in Korea. Therefore, it is necessary to investigate with various studies to reduce pedestrian-vehicle crashes. In this study, the cognitive characteristics affecting pedestrian-vehicle crashes were considered by applying deep learning semantic segmentation. The main results are as follows. First, it was found that the risk of pedestrian-vehicle crashes increased when the ratio of buildings among cognitive factors increased and when the ratio of vegetation and the ratio of sky decreased. Second, the humps were shown to reduce the risk of pedestrian-related collisions. Third, the risk of pedestrian-vehicle crashes was found to increase in areas with many neighborhood roads with lower hierarchy. Fourth, traffic lights, crosswalks, and traffic signs do not have a practical effect on reducing pedestrian-vehicle crashes. This study considered existing physical neighborhood environmental factors as well as factors in cognitive aspects that comprise the visual elements of the streetscape. In fact, the cognitive characteristics were shown to have an effect on the occurrence of pedestrian- related collisions. Therefore, it is expected that this study will be used as fundamental research to create a pedestrian-friendly urban environment considering cognitive characteristics in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.295-306
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• 2008

The Intersection is inner traffic facilities and the space where the roads are intersected and connected. And also, the Intersection is the decision-making section for drivers to select the route according to the geometric structure and operation method. However decision-making section cause to raise car accidents rate because it imposes a heavy burden on drivers. In that reason, many countries such as Europe use the Roundabouts to reduce the numbers of decision making and collision. In Korea, the kinds of method are just introduced and it is using now but there are no exact standards. Hence, this study suggests the process to evaluate and determine the types of Intersection which are based on the traffic flow (congestion) and traffic safety (accidents). Firstly, this study presents the number of accident at each Intersection which is depended on the traffic volume. Secondly, this study calculates and analysis the accident at signalized Intersection, non-signalized Intersection and Roundabout by TSIS-NETSIM program. Thirdly, this study concludes the best suitable Intersection type through the materials which are mentioned before.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.821-829
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• 2006

Traffic load comprise primary input to pavement design causing pavement damage. therefore it should be proceeded suitable traffic load distribution modeling for pavement design and analysis. Traffic load have been represented by equivalent single axle loads (ESALs) which convert mixed traffic stream into one value for design purposes. But there are some limit to apply ESALs to other roads because it is empirical value developed as part of the original AASHO(American Association of State Highway Officials) road test. There have been many efforts to solve these problems. Several leading country have implemented M-E(Mechanistic-Empirical) design procedures based on mechanical concept. As a result, they established traffic load quantification method using load distribution model known as Axle Load Spectra. This paper details Axle Load Spectra and presents axle load distribution model based on normal mixture distribution function using truck load data collected by WIM system installed in national highway. Axle load spectra and axle load distribution model presented in this paper could be useful for basic data when making traffic load quantification plan for pavement design, overweight vehicle permit plan and pavement maintenance cost plan.