• Journal of Korean Society of Transportation
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• v.31 no.1
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• pp.3-13
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• 2013

This paper reviews the characteristics of dilemma zone by analysing the influence exerted by actual location of intersection traffic signal on behaviour of drivers approaching signalized intersection in urban area. The analysis of approach speed was based upon a 'before and after' comparison, measured at three sites where the locations of traffic signals were changed. The study demonstrated that, when traffic signal changed to yellow, the scales of dilemma zone were narrowed in case of stopping cars by moving up the starting point of the dilemma zone due to lowered spot speed. On the other hand, in case of passing cars, the end points of dilemma zone were moved further out to the rear due to increased spot speed. Therefore, changing traffic signal locations could make an impact to increase intersection safety through reducing the scales of dilemma zone. This study also found that, in cases involving vehicles with similar approach speeds, spot speeds could be differentiated following the change of signal locations due to the fact that there can be greater differences in both braking point and deceleration rate. Thus, when considering the appropriate measuring of dilemma zone, 'spot speed' rather than 'approach speed' appeared to be more appropriate criterion.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.