• Journal of Korean Society of Transportation
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• v.36 no.3
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• pp.216-228
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• 2018

In this study analyzed the ponding changing of plastic deformation section follwed time development to apply weather, geometry and traffic data in additon to time development to improve road management service and safety of roads during or after rain. After We selected an 8.3km section of old national highway the Seongnam-Janghowon section and created a three-demensional surface of terrain through the numerical transformantion of design drawing data, with reflection the linear data of the same coordinate system in order to describe more realistic roads, we design additional structures with shading above roads. The altitude and azimuth of the sun were calculated and set based on the longitude and latitude data of the survey line for the analysis of the sun rate, and the daylight impact zone was visualized by setting the shaded time to an interval of 1 hour and the shade rate of the corresponding section. In addition, the evaporation volume calculated from weather data such as temperature, humidity, radiant energy, and road temperature analyzes together, it will use the way of a safer and more efficient road management as grasping the ponding changing more efficent in time development.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.6
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• pp.38-44
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• 2019

A Convolution Neural Network(CNN) model was utilized to detect surface cracks in asphalt concrete pavements. The CNN used for this study consists of five layers with 3×3 convolution filter and 2×2 pooling kernel. Pavement surface crack images collected by automated road surveying equipment was used for the training and testing of the CNN. The performance of the CNN was evaluated using the accuracy, precision, recall, missing rate, and over rate of the surface crack detection. The CNN trained with the largest amount of data shows more than 96.6% of the accuracy, precision, and recall as well as less than 3.4% of the missing rate and the over rate.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.585-590
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• 2017

Vehicle behavior is determined by forces and a torques generated at the ground contact surface of the tire. Various tire models are used to calculate the forces and torques acting on the tire. The brush model calculates the forces and torques with fewer coefficients than other tire models. However, owing to fewer degrees of freedom in calculating the forces, this model has limitations in precisely expressing measured data. In this study, this problem was addressed by adding the least parameters to the friction coefficient and tire properties of the brush model, and the proposed model was validated.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.55-64
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• 2018

In this paper, we propose fully convolutional neural networks based automatic detection of a pothole that directly causes driver's safety accidents and the vehicle damage. First, the training DB is collected through the camera installed in the vehicle while driving on the road, and the model is trained in the form of a semantic segmentation using the fully convolutional neural networks. In order to generate robust performance in a dark environment, we augmented the training DB according to brightness, and finally generated a total of 30,000 training images. In addition, a total of 450 evaluation DB was created to verify the performance of the proposed automatic pothole detection, and a total of four experts evaluated each image. As a result, the proposed pothole detection showed robust performance for missing.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.150-156
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• 2011

Smart Highway is a next generation highway that significantly improves a traffic safety, reduces incidence of traffic accidents, and supports intelligent and convenient driving environments so that drivers can drive at high speeds in safety. In order to implement smart highway, it is required to gather a large amount of data including conditions of a road and the status of vehicles, and other useful data. To provide situation information of highway, it has been gathered traffic information using optical sensors(CCTV, etc.). However, this technique has problems such as the problem of information gathering, lack of accuracy depending on weather conditions and limitation of maintenance. It needs radar system which has not effect on environmental change and algorithm processing technique in order to provide information for a safety driving to driver and car. In this paper, it is used radar with 9.4GHz to test performance of a road surface and developed radar system for detecting test. And we compared and analyzed a performance of data acquired from each radar through computer simulation.

• Korean Journal of Environment and Ecology
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• v.28 no.3
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• pp.365-373
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• 2014

This study is to analyze the physical environment and conduct spatial data for bicycle road system in changwon. Index for evaluation index was developed based on literatures. Then the level of importance and weight have been modified through experts review. Finally, index with eight categories such as greenness(40% over), bicycle road connectivity(1.8, 9.8%), road type bike(bicycle lane, 24.4%), pave type(asphalt 72.5%), illegal parking(none, 93.9%), bike road surface visibility(exist, 46.8%), vehicle speed limits(30km, under), vehicle traffic(500/hr under, 44.3%) have been applied to empirical investigation. Collected data has been hierarchically classification by ArcGIS Program. The Highest grades(score 31-35, level 1) occupied 35% of target destination. High level of greenness and load type has contributed to high score. In addition, average level of greenness of those destination was 35% and higher, which provide high degree of security and freshness for bicycle riding. Meanwhile, lowest level(level 5, which earned 15 point or less) occupied 24.5%. illegal parking, low level of greenness, and no surface sign caused low score.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.55-63
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• 2021

The roughness of the road is an important factor directly connected to the ride comfort, and is an evaluation item for functional evaluation and pavement quality management of the road. In this study, data on the road surface were acquired using the latest 3D geospatial information construction technology of ground LiDAR, drone photogrammetry, and drone LiDAR, and the accuracy and roughness of each method were analyzed. As a result of the accuracy evaluation, the average accuracy of terrestrial LiDAR were 0.039m, 0.042m, 0.039m RMSE in X, Y, Z direction, and drone photogrammetry and drone LiDAR represent 0.072~0.076m, 0.060~0.068m RMSE, respectively. In addition, for the roughness analysis, the longitudinal and lateral slopes of the target section were extracted from the 3D geospatial information constructed by each method, and the design values were compared. As a result of roughness analysis, the ground LiDAR showed the same slope as the design value, and the drone photogrammetry and drone LiDAR showed a slight difference from the design value. Research is needed to improve the accuracy of drone photogrammetry and drone LiDAR in measurement fields such as road roughness analysis. If the usability through improved accuracy can be presented in the future, the time required for acquisition can be greatly reduced by utilizing drone photogrammetry and drone LiDAR, so it will be possible to improve related work efficiency.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.104-114
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• 2016

Potholes have caused diverse problems such as wheel damage and car accident. A pothole detection technology is the most important to provide efficient pothole maintenance. The previous pothole detections have been performed by manual reporting methods. Thus, the problems caused by potholes have not been solved previously. Recently, many pothole detection systems based on video cameras have been studied, which can be implemented at low costs. In this paper, we propose a new pothole detection algorithm based on saliency map information in order to improve our previously developed algorithm. Our previous algorithm shows wrong detection with complicated situations such as the potholes overlapping with shades and similar surface textures with normal road surfaces. To address the problems, the proposed algorithm extracts more accurate pothole regions using the saliency map information, which consists of candidate extraction and decision. The experimental results show that the proposed algorithm shows better performance than our previous algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.111-122
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• 2012

There has been a strong demand for providing diverse services to drivers utilizing existing ITS infrastructure. To this end, this study is aiming at improving the accuracy of a variable speed limit system by determining recommended speeds for the system utilizing the information from Urban Traffic Information System(UTIS) and Weather Information System(WIS). In order to determine appropriate speed limits under inclement weather conditions for the variable speed limit system, this study examined three methods: i) the method utilizing the information from WIS, ii) the method utilizing the information from UTIS, and iii) the method which combines the information from WIS and UTIS using different weights for diverse weather conditions. Finally, this study selected the third method which determines an appropriate speed limit using the relationship between the vehicle operating speed and the minimum stopping distance which is estimated using the existing speed limit, surface coefficient of friction and superelevation.