• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.95-105
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• 2019

In recent years, image processing techniques for detecting road surface damaged spot have been actively researched. Especially, it is mainly used to acquire images through a smart phone or a black box that can be mounted in a vehicle and recognize the road surface damaged region in the image using several algorithms. In addition, in conjunction with the GPS module, the exact damaged location can be obtained. The most important technology is image processing algorithm. Recently, algorithms based on artificial intelligence have been attracting attention as research topics. In this paper, we will also discuss artificial intelligence image processing algorithms. Among them, an object detection method based on an region-based convolution neural networks method is used. To improve the recognition performance of road surface damage objects, 600 road surface damaged images and 1500 general road driving images are added to the learning database. Also, supervised learning using background object recognition method is performed to reduce false alarm and missing rate in road surface damage detection. As a result, we introduce a new method that improves the recognition performance of the algorithm to 8.66% based on average value of mAP through the same test database.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.517-528
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• 2007

Domestic or international existing researches regarding rail damage factors are focused on laying, vehicle conditions, driving speed and driving habits and overlook characteristics of track structure (elasticity, maintenance etc). Also in ballast track, as there is no special lateral spring stiffness of track also called as ballast lateral resistance in concrete track, generally, existing study shows concrete track has 2 time shorter life cycle for rail replacement than ballast track due to abrasion. As a result of domestic concrete track design and operation performance review, concrete track elasticity is lower than track elasticity of ballast track resulting higher damage on rail and tracks. Generally, concrete track has advantage in track elasticity adjustment than ballast track and in case of Europe, in concrete track design, it is recommended to have same or higher performance range of vertical elastic stiffness of ballast track but domestically or internationally review on lateral spring stiffness of track is very minimal. Therefore, through analysis of service line track on site measurement and analysis on performance of maintenance, in this research, dynamic characteristic behaviors of commonly used ballast and concrete track are studied to infer elasticity of service line track and experimentally prove effects of track lateral spring stiffness that influence curved rail damage as well as correlation between track elasticity by track system and rail damage to propose importance of appropriate elastic stiffness level for concrete and ballast track.