• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.671-672
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• 2010

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.3
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• pp.61-70
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• 2001

This paper presents a new method to extract semi-automatically roads from high spatial resolution satellite imagery. This method is based both on wavelet transform and on multiresolution analysis combined in the "$\grave{a}$ trous" algorithm. As an urban road network consists on different classes of streets, multiresolution processing allows to extract the streets class by class. The method was applied to a KVR-1000 image on a part of Busan Metropolitan City. The method was carried out for the road extraction of three different widths and it succeeded in extracting good fitted strips. The accuracy analysis for three types of streets was also performed. The overall accuracy in 4 pixels of width is 80.5%. The result suggests that this method can be used to update road networks in the studied urban network. In summary, the multiresolution approach based on the wavelet transform, used in this study, is regarded as one of effective methods to extract urban road network from high spatial resolution satellite images.

• International Journal of Highway Engineering
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• v.13 no.1
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• pp.59-67
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• 2011

It can prevent traffic accidents in a way as taking precautionary measures for road safety at signalized intersection in advance. Particularly, traffic accidents can be reduced at relatively low cost without redesigning alignments. That is 'Positive Guidance method' which can help prevent traffic accidents through improvement of road facilities at signalized intersection. In this study, potentially higher hazardous signalized intersection due to speeding was selected through site investigation. Field analysis at designated section was conducted and devised a plan for improvements of road facilities. Subjects drove in driving simulator in 3-D virtual reality of designated intersection. Based on data from simulator, statistical analysis(t-verification) was conducted for 'Before and After effectiveness' of speed reduction. As a result, it indicates that speed reduction was effective after improvements at each spot in driving simulator. In the future, hazardous signalized intersections which can be applied for PG method will be effective for road safety based on this research.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.143-151
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• 2017

Recently Korea has presented carbon emission reduce goal of 37% compare to BAU until 2030 according to Paris Agreement in order to correspond to climate change. For this, researchers need to study positively on construction industry that emit $CO_2$ of $3^{rd}$ volume of 28 industry classification. This study calculated environmental load by LCA using the road part except tunnel and bridge among national road cases completed already. After selecting representative type of large construction type based on environmental emission, earth works, drainage works and paving works took up 84%. And this study analyzed the environmental emission feature of each detail construction type after selecting representative type each detail construction type. Utilization of each construction type emission attribute to environmental load during national road construction, will be helpful in making decision of eco-friendly national road construction based on environmental emission.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1437-1444
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• 2020

With the development of computer vision systems, many advances have been made in the fields of surveillance, biometrics, medical imaging, and autonomous driving. In the field of autonomous driving, in particular, the object detection technique using deep learning are widely used, and the paved road detection is a particularly crucial problem. Unlike the ROI detection algorithm used in general object detection, the structure of paved road in the image is heterogeneous, so the ROI-based object recognition architecture is not available. In this paper, we propose a deep neural network architecture for atypical paved road detection using Semantic segmentation network. In addition, we introduce the multi-scale semantic segmentation network, which is a network architecture specialized to the paved road detection. We demonstrate that the performance is significantly improved by the proposed method.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.1-6
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• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.113-122
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• 2024

In this paper, we conducted a thorough investigation of existing simulators for running simulations of Vehicular Adhoc Networks (VANET) in realistic road environments, such as digital twins. After careful consideration, we chose a simulator that combines OSM (OpenStreetMap), SUMO (Simulation of Urban MObility), and OMNeT++ due to its open-source nature and efficient performance. Using this integrated simulator, we carried out VANET simulations in both simple virtual road environments and realistic road environments. Our findings revealed significant differences in VANET performance between the two types of environments, emphasizing the need to consider realistic road and traffic environments for reliable VANET operation. Furthermore, our simulations demonstrated significant performance variability, with performance degradation observed as vehicle density decreased and dynamic changes in network topology increased. These results underscore the importance of digital twin-based approaches in VANET research, highlighting the need to simulate real-world road and traffic conditions rather than relying on simple virtual road environments.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.2 s.20
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• pp.29-37
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• 2002

Many of the traffic accidents on roads are a result of alignment defect of the roads. This alignment of the road needs to lie analyzed with accuracy for improving design of road, and it needs the design drawing of road, and coordinates of the main point. Accordingly, in this study the precision of location based on existing design drawing was compared with the data acquired by the combination of RTK GPS/GLONASS. The result of study is included within range 5cm, we would like to propose an effective and useful approach method to utilize the satellite for road alignment information system by evaluating the represented road alignment.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.59-77
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• 2007

Advances in mobile techknowledges and supporting techniques require an effective representation and analysis of moving objects. Similarity search of moving object trajectories is an active research area in data mining. In this paper, we propose a trajectory search algorithm for spatio-temporal similarity of moving objects on road network. For this, we define spatio-temporal distance between two trajectories of moving objects on road networks, and propose a new method to measure spatio-temporal similarity based on the real road network distance. In addition, we propose a similar trajectory search algorithm that retrieves spatio-temporal similar trajectories in the road network. The algorithm uses a signature file in order to retrieve candidate trajectories efficiently. Finally, we provide performance analysis to show the efficiency of the proposed algorithm.

• Korean Journal of Health Education and Promotion
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• v.27 no.1
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• pp.49-60
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• 2010

Objectives: The purpose was to evaluate the effectiveness of participatory road safety education program for the lower grade in elementary schools. This program was developed based on the Activated Health Education model. Methods: Study design was compromise experimental group pre-post design. 456 students in 8 schools were nonrandomly assigned to study group(n=224) or control(n=232). The students finished self-administered questionnaire before and after education. Collected data was analyzed by using the SPSS. Results: 1. The common relevant factors in road safety knowledge and attitude were 'residential state' and 'experience of traffic accident'. 2. The knowledge showed that the case and control scored at 6.48 and 6.41 points before. After this intervention, the case and control scored at 8.38 and 6.51. The difference of the case was significant(p<0.001). 3. The attitude showed that the case and control scored at 19.67 and 19.63 before. After this, the case and control scored at 19.86 and 19.63. The difference of the case was significant(p<0.05). Conclusion: In order to implement the road safety education, children's socio-demographic characteristics were considered. Because this education was effective in both improving knowledge and attitude and bringing interest, various participatory program will be applied in road safety education for children.