• Journal of Korean Society of Transportation
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• v.34 no.6
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• pp.489-498
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• 2016

The objective of this study is to develop road traffic accident model involving child pedestrian especially at school zones and its surrounding area. The analysis is based upon traffic accident data collected near sixty elementary schools in City of Cheongju during 2012 and 2014. This study results in two statistical models ; one is to predict the number of road traffic accidents involving children, and the other is to predict EPDO(Equivalent Prperty Damage Only). These models are represented as Poisson models. which are statistically significant with the likelihood ratios of 0.533 and 0.273. The common explanatory variables of these models are the ratio of road section with more than 4 lanes, the number of entrance and exit, the number of signalized crosswalk in school zone, the number of school zone signage including road surface marking, and the number of speed limit signs. The specific variables are the length of road stretch in school zone, the number of reflector mirrors, and the number of signalized crosswalk outside school zone. It is concluded that these types of road safety facilities can reduce the number of traffic accidents involving children at school zones and its surrounding area.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.29-40
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• 2008

Police agency is indicated that the number of children's traffic accident is tending downwards, however about one child is dead every day due to traffic accident. Major parts of the accidents happen during walking; among the rest, jaywalking is the biggest reason. Many accidents take plate on the road to school or near the home so government legislate children safeguard zone at 1995. According to the legislation, drivers have to reduce speed and there are safety facilities for children at children safeguard zone. This study finds the problems of children safeguard zone and suggest more effective and quantitative method for children safeguard zone. Firstly this study grasps the characters of children's pattern movement and children's traffic accident at children safeguard zone and then divides specific danger factors of children's traffic accident at children safeguard zone. Secondly, each factor is given danger level depending on danger degree and suggests effective method for assignment standard of children safeguard zone using all of these things.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.66-72
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• 2014

Histogram of oriented gradient (HOG) feature is widely used in vision-based pedestrian detection. The interpolation is the most important technique in HOG feature calculation to provide high detection rate. In interpolation technique of HOG feature calculation, two nearest orientation bins to gradient orientation for each pixel and the corresponding weights are required. In this paper, therefore, an efficient gradient orientation bin and weight calculation circuit for HOG feature is proposed. In the proposed circuit, pre-calculated values are defined in tables to avoid the operations of tangent function and division, and the size of tables is minimized by utilizing the characteristics of tangent function and weights for each gradient orientation. Pipeline architecture is adopted to the proposed circuit to accelerate the processing speed, and orientation bins and the corresponding weights for each pixel are calculated in two clock cycles by applying efficient coarse and fine search schemes. Since the proposed circuit calculates gradient orientation for each pixel with the interval of $1^{\circ}$ and determines both orientation bins and weights required in interpolation technique, it can be utilized in HOG feature calculation to support interpolation technique to provide high detection rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.6A
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• pp.341-350
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• 2003

In this paper, we evaluate the performance of a moving average (MA) channel estimation filter when space-time block coding transmit diversity (STBC-TD) is applied to the wideband direct sequence code division multiple access (WCDMA) forward link. And we present the infinite impulse response (IIR) filter scheme that can reduce the required memory buffer and the channel estimation delay time. This paper also compares the performance between MA filter scheme and IIR filter scheme in various Rayleigh fading channel environments through the bit error rate (BER) and the frame error rate (FER). Extensive computer simulation results show that transmission with STBC-TD provides a significant gain in performance over no transmit diversity technique, particularly at pedestrian speeds. If STBC-TD technique is employed in the channel estimator based on MA filter, it provides considerable performance gains against Rayleigh fading and reduces the optimum filter tap number. Consequently, the channel estimation delay time and the complexity of the receiver are reduced. In addition, the channel estimator based on IIR filter has the advantages such as little memory requirement and no delay time compared to the MA scheme. However, IIR filter coefficients is very sensitive to the mobile speed change and it exerts a serious influence upon the performance. For that reason, it is important to set uP the optimum IIR filter coefficients.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.157-165
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• 2020

Aiming at the development of neural network and self-driving data set, it is also an idea to improve the performance of network model to detect moving objects by dividing the data set. In Darknet network framework, the YOLOv4 (You Only Look Once v4) network model was used to train and test Udacity data set. According to 7 proportions of the Udacity data set, it was divided into three subsets including training set, validation set and test set. K-means++ algorithm was used to conduct dimensional clustering of object boxes in 7 groups. By adjusting the super parameters of YOLOv4 network for training, Optimal model parameters for 7 groups were obtained respectively. These model parameters were used to detect and compare 7 test sets respectively. The experimental results showed that YOLOv4 can effectively detect the large, medium and small moving objects represented by Truck, Car and Pedestrian in the Udacity data set. When the ratio of training set, validation set and test set is 7:1.5:1.5, the optimal model parameters of the YOLOv4 have highest detection performance. The values show mAP50 reaching 80.89%, mAP75 reaching 47.08%, and the detection speed reaching 10.56 FPS.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.100-118
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• 2021

In this study, PM accidents (1,603case) and bicycle accidents (14,672case) that occurred in the last three years were analyzed to determine the characteristics of PM traffic accidents. In particular, PM traffic accidents were divided into perpetrators and victims to determine the characteristics in detail. For PM accidents, the analysis was conducted on the status of each road grade, road type, weather condition, accident type, day and night occurrence, and vehicle type. The number of PM accidents that occurred in 2019 increased by 129%, and deaths increased by more than 200% compared to the previous year. The proportion of pedestrian accidents among PM traffic accidents was higher than that of bicycle accidents. Therefore, regulations on PM traffic are necessary. For the 20 deaths of PM, a detailed analysis was conducted to analyze the factors of traffic accidents. PM fatalities occurred in 50% of vehicle accidents, and 7 out of 10 vehicle accidents occurred at night. This is believed to have been caused by falling or overturning due to an obstacle, such as a depression in the road pavement or a speed bump.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.10
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• pp.603-612
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• 2016

This study will observe the following subjects based on the railroad: First, the development of St. Pancreas Station, which is the gateway to London from other Europe continent, and King's Cross Station, which connects all the intercity within London. Second, the planning characteristic of urban regeneration case which was driven from Camden district, the center of King's Cross Central, which is located in between the two stations, St. Pancreas and King's Cross. Third, based on the the two stations and urban regeneration, this study attempts to investigate the direction of urban regenerating plan and its detailed strategy. As a result, King's Cross Station, St. Pancreas Station and the King's Cross Central area, which is an adjacent area from the two stations, were a slum for a long time. However, the two close stations played a role as the United Kindom and London's railroad network by sharing the common denominator of having the international high-speed railway among the Europe continent and being the connection of National railroads within London. Eventually, based on such potential of railroad traffic, King's Cross Central area was newly regenerated. The consequence of this study has shown that not only the physical modernization of buildings, implementation of compact railroad network supporting both ground and underground of each area or traffic connection was organized in London, but also secured the pedestrian way for easier transfer and planned and allocated facilities by considering citizen's publicness and multilateral use.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.41-51
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• 2023

In this paper, we propose a technique of multi-time window feature extraction for anger detection in gait data. In the previous gait-based emotion recognition methods, the pedestrian's stride, time taken for one stride, walking speed, and forward tilt angles of the neck and thorax are calculated. Then, minimum, mean, and maximum values are calculated for the entire interval to use them as features. However, each feature does not always change uniformly over the entire interval but sometimes changes locally. Therefore, we propose a multi-time window feature extraction technique that can extract both global and local features, from long-term to short-term. In addition, we also propose an ensemble model that consists of multiple classifiers. Each classifier is trained with features extracted from different multi-time windows. To verify the effectiveness of the proposed feature extraction technique and ensemble model, a public three-dimensional gait dataset was used. The simulation results demonstrate that the proposed ensemble model achieves the best performance compared to machine learning models trained with existing feature extraction techniques for four performance evaluation metrics.