• Korean Journal of Construction Engineering and Management
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• v.14 no.4
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• pp.164-171
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• 2013

In urban planning, road facility is used not only for the transportation purpose but also for the utility line space purpose such as electrical, gas, tele communication, heating, water, sewer, and so on. However, since these utilities are built by many different groups, it becomes very difficult to communicate each other. Delay in one party can cause another party's schedule delay but they don't commuicate often. Also, some delay in utility work can cause frequent pavement cut. And, this will impact on construction cost, schedule delay, low quality, user complain and cost. This study developed spatiotemporal decision model to prevent prequent utility cut for mega project such as new urban development project. In addition, this study developed utility cut management system to manage utility cut schedule under pavement. Finally, developed system was applied to new urban development project and verified there effectiveness.

• The Transactions of the Korea Information Processing Society
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• v.4 no.3
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• pp.859-870
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• 1997

Computerized map reading system is one of the most important application areas in the image processing.A map databaes can be used for a wide range of scial activities such as narural resource assessment,regional plan-ming,and reaffic nabigation system. The road segments,however,are extracted as briken in the area where they are overlapped and interupted by chracters and symbols.Few approaches have been taken to complete road segnents interupted by map symbols.In this paper,a movel approach for the extracation and completion of road segements interupted by map symbols is proposed using neural networks.The system is applied to 1/25,000 scaled maps published by the Grographical Survey Unstitute of Ministry of Construction of Korea.It will be shown that the system can extract and reconstruct road segmetns for the various areas of maps sucessfully.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.119-127
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• 2019

In this study, we investigated the application of deep learning to the manufacturing process of traffic and road signs which are constituting the road layer in map production with 1 / 1,000 digital topographic map. Automated classification of road traffic sign images was carried out through construction of training data for images acquired by using transfer learning which is used in image classification of deep learning. As a result of the analysis, the signs of attention, regulation, direction and assistance were irregular due to various factors such as the quality of the photographed images and sign shape, but in the case of the guide sign, the accuracy was higher than 97%. In the digital mapping, it is expected that the automatic image classification method using transfer learning will increase the utilization in data acquisition and classification of various layers including traffic safety signs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.106-118
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• 2019

In recent years, research and development on image object recognition using artificial intelligence have been actively carried out, and it is expected to be used for road maintenance. Among them, artificial intelligence models for object detection of road surface are continuously introduced. In order to develop such object recognition algorithms, a backbone network that extracts feature maps is essential. In this paper, we will discuss how to select the appropriate neural network. To accomplish it, we compared with 4 different deep neural networks using 6,000 road surface damage images. Based on three evaluation methods for analyzing characteristics of neural networks, we propose a method to determine optimal neural networks. In addition, we improved the performance through optimal tuning of hyper-parameters, and finally developed a light backbone network that can achieve 85.9% accuracy of road surface damage classification.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.104-113
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• 2019

The road management institute needs lots of cost to repair road surface damage. These damages are inevitable due to natural factors and aging, but maintenance technologies for efficient repair of the broken road are needed. Various technologies have been developed and applied to cope with such a demand. Recently, maintenance technology for road surface damage repair is being developed using image information collected in the form of a black box installed in a vehicle. There are various methods to extract the damaged region, however, we will discuss the image recognition technology of the deep neural network structure that is actively studied recently. In this paper, we introduce a new neural network which can estimate the road damage and its location in the image by region-based convolution neural network algorithm. In order to develop the algorithm, about 600 images were collected through actual driving. Then, learning was carried out and compared with the existing model, we developed a neural network with 10.67% accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.535-544
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• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.71-82
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• 2021

In this study, a novel method for monitoring road pavements using the Mobile Mapping System (MMS) and a deep learning crack detection system was presented. Furthermore, an optimal maintenance method through economic analysis was presented targeting the pavement section of Sejong City. As a result of monitoring the pavement conditions, it was confirmed that the pavement ratings were good in the order of national highways, municipal roads, and roads of provinces. In addition, economic analysis using the pavement deterioration model showed that micro-surfacing, one of the preventive maintenance methods, is the most economical in terms of maintenance costs and user benefits. The results of this study are expected to be used as fundamental reference for local governments' infrastructure management plans.

• Journal of Platform Technology
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• v.9 no.4
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• pp.3-9
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• 2021

In this paper, a graph data model is introduced to effectively recognize the situation between each object on the road detected by vehicles or road infrastructure sensors. The proposed method builds a graph database by modeling each object on the road as a node of the graph and the relationship between objects as an edge of the graph, and updates object properties and edge properties in real time. In this case, the relationship between objects represented as edges is set when there is a possibility of approach between objects in consideration of the position, direction, and speed of each object. Finally, we propose a spatial indexing technique for graph nodes and edges to update the road object graph database represented through the proposed graph modeling method continuously in real time. To show the superiority of the proposed indexing technique, we compare the proposed indexing based database update method to the non-indexing update method through simulation. The results of the simulation show the proposed method outperforms more than 10 times to the non-indexing method.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.51-62
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• 2022

AI-based speed prediction studies have been conducted quite actively. However, while the importance of explainable AI is emerging, the study of interpreting and reasoning the AI-based speed predictions has not been carried out much. Therefore, in this paper, 'Explainable Deep Graph Neural Network (GNN)' is devised to analyze the speed prediction and assess the nearby road influence for reasoning the critical contributions to a given road situation. The model's output was explained by comparing the differences in output before and after masking the input values of the GNN model. Using TOPIS traffic speed data, we applied our GNN models for the major congested roads in Seoul. We verified our approach through a traffic flow simulation by adjusting the most influential nearby roads' speed and observing the congestion's relief on the road of interest accordingly. This is meaningful in that our approach can be applied to the transportation network and traffic flow can be improved by controlling specific nearby roads based on the inference results.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.4A
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• pp.197-206
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• 2012

It is found the first case of broad interpretation of the crash analysis in MASH (Manual for Assessing Safety Hardware, AASHTO, 2009) which is the guideline of roadside safety features in United States. They introduced the procedure of calculating 1,500 kg sedan safety index from the 2,270 kg pick-up truck crash test for crash cushion. First, following MASH's method, calculate 0.9 ton vehicle crash data and safety index using 1.3 ton vehicle crash test data and compare with actual 0.9 ton vehicle crash test data. results show that actual test data and the data calculated by MASH's method have great difference. Second, analyse the cause and develop new method. Proposed method can estimate not only the lighter vehicle (0.9 ton) crash data from the heavier vehicle (1.3 ton) crash test but also heavier vehicle (1.3 ton) data from lighter vehicle (0.9 ton) test. This method is superior to MASH's method and has stronger theoretical foundation. This paper proves the efficiency and the accuracy of new broad interpretation method using crash test data and investigates the principle.