• Journal of Digital Convergence
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• v.17 no.12
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• pp.345-352
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• 2019

This study aims to analyze the factors affecting the length of stay in elderly pediatric inpatients in traffic accidents. We used Korean National Hospital Discharge In-depth Injury data on the discharged from 2012 to 2016. Statistically significant factors affecting the length of stay are admission route, Charlson Comorbidity Index(CCI), injury parts, operation, results, hospital area, and beds for hospitals. The length of stay was shorter in the case of the admission route of the outpatient department than the emergency room, the results were not improved or death rather than improved, and the bed size was 500-999 beds or over 1000 beds rather than 100-299 beds. However, the length of stay was longer in the case of CCI score was 1-2 or over 3 rather than 0, injury parts were other parts rather than head/neck, when the operation was yes, and when the hospital area was a province, metropolitan rather than Seoul. This study intends to understand the medical characteristics of inpatient to prevent pedestrian traffic accidents in accordance with the population aging. Based on this finding, we wish to be used as the basic data for the establishment of policies to effectively manage traffic safety and medical resources in consideration of the characteristics of the elderly people.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5948-5971
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• 2019

Estimating loss rates with the packet traces captured from some point in the middle of the network has received much attention within the research community. Meanwhile, existing intermediate-point methods like [1] require the capturing system to capture all the TCP traffic that crosses the border of an access network (typically Gigabit network) destined to or coming from the Internet. However, limited to the performance of current hardware and software, capturing network traffic in a Gigabit environment is still a challenging task. The uncaptured packets will affect the total number of captured packets and the estimated number of packet losses, which eventually affects the accuracy of the estimated loss rate. Therefore, to obtain more accurate loss rate, a method of strengthening packet loss measurement from the network intermediate point is proposed in this paper. Through constructing a series of heuristic rules and leveraging the binomial distribution principle, the proposed method realizes the compensation for the estimated loss rate. Also, experiment results show that although there is no increase in the proportion of accurate estimates, the compensation makes the majority of estimates closer to the accurate ones.

• ETRI Journal
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• v.42 no.5
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• pp.700-711
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• 2020

Because of the rapid increase of mobile traffic, flexible broadband supportive unmanned aerial vehicle (UAV)-based 6G mobile networks using free space optical (FSO) links have been recently proposed. Considering the advancements made in UAVs, big data processing, and artificial intelligence precision control technologies, the formation of an additional wireless network based on UAV aerial platforms to assist the existing fixed base stations of the mobile radio access network is considered a highly viable option in the near future. In this paper, a combined time bound optimization scheme is proposed that can adaptively satisfy the control and communication time constraints as well as the processing time constraints in FSO-based 6G UAV aerial networks. The proposed scheme controls the relation between the number of data flows, input data rate, number of worker nodes considering the time bounds, and the errors that occur during communication and data processing. The simulation results show that the proposed scheme is very effective in satisfying the time constraints for UAV control and radio access network services, even when errors in communication and data processing may occur.

• Journal of Advanced Navigation Technology
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• v.19 no.4
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• pp.305-310
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• 2015

A controlling system for airport refueling facilities is connected to sensors which collect various informations. Informations which are transmitted at a high speed in various sensors are processed by a dedicated software in the controlling system. The problems of system maintenance and network traffic caused by the use of dedicated software reduce the efficiency of the system operation. Therefore, a web-based system that can be accessed using the Internet environment is required. In this paper, we propose a system showing web-based real-time informations. To do this, we change the function of the communication by each sensor to a facade structure, and design a system for transferring web-based real-time informations. Also we propose data-driven infographics for displaying the real-time big data information at a high speed on the web. Finally, we compare and analyse the proposed system between the existing system and show that our system can effectively display the real-time information on the web.

• Journal of Korean Neurosurgical Society
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• v.60 no.6
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• pp.710-716
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• 2017

Objective : Among pediatric injury, brain injury is a leading cause of death and disability. To improve outcomes, many developed countries built neurotrauma databank (NTDB) system but there was not established nationwide coverage NTDB until 2009 and there have been few studies on pediatric traumatic head injury (THI) patients in Korea. Therefore, we analyzed epidemiology and outcome from the big data of pediatric THI. Methods : We collected data on pediatric patients from 23 university hospitals including 9 regional trauma centers from 2010 to 2014 and analyzed their clinical factors (sex, age, initial Glasgow coma scale, cause and mechanism of head injury, presence of surgery). Results : Among all the 2617 THI patients, total number of pediatric patients was 256. The average age of the subjects was 9.07 (standard deviation${\pm}6.3$) years old. The male-to female ratio was 1.87 to 1 and male dominance increases with age. The most common cause for trauma were falls and traffic accidents. Age (p=0.007), surgery (p<0.001), mechanism of trauma (p=0.016), subdural hemorrhage (SDH) (p<0.001), diffuse axonal injury (DAI) (p<0.001) were statistically significant associated with severe brain injury. Conclusion : Falls were the most common cause of trauma, and age, surgery, mechanism of trauma, SDH, DAI increased with injury severity. There is a critical need for effective fall and traffic accidents prevention strategies for children, and we should give attention to these predicting factors for more effective care.

• The Journal of Information Systems
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• v.31 no.3
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• pp.47-65
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• 2022

Purpose With the recent development of Big Data and Artificial Intelligence technology, self-driving technology has developed into three stages (partial self-driving) or four stages (conditional self-driving), it is expected to bring a new paradigm to transportation in the city. Although many researchers are researching related technologies, there is no research on self-driving for disabled persons. In this study, the basic research was conducted based on the assumption that the shared self-driving car used by the disabled person is similar to the special transportation currently driving. Design In this study, data analysis and machine learning techniques were utilized to analyze the mobility patterns of disabled persons by type and to search for leading factors affecting the traffic volume of special transportation. Findings The study found that external physical disorders and developmental disorders often visit general welfare centers, internal organ disorders often visit general hospitals, and the elderly and mental disorders have various destinations. In addition, machine learning analysis showed that the main transportation routes for the disabled person use arterial roads and auxiliary arterial roads and that the ratio of building usage-related variables affecting the use of special transportation for a disabled person is high. In addition, the distance to the subway and bus stops was also mentioned as a meaningful variable. Based on these analysis results, it is expected that the necessary infrastructure for shared self-driving cars for disability person traffic will be used as meaningful research data in the future.

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.3-11
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• 2013

Under the consumption of bad weather situation affects traffic flows, the study scope is focused on highway capacity and speed variations among other highway traffic flow characteristic changes according to snowfall density. Thus, this study carried out through the data collection and statistical analysis by focusing on capacity and speed changes. Traffic volume, speed and density were selected as factors to explain the property change of a traffic flow for analysis, and 7 basic sections such as 3 highways in Gyeonggi-do and 4 highways near the meteorological observatory were selected as survey points for data collection. Snowfall levels were classified into 3 steps(Light, Medium, Heavy Snow) to analyze the capacity change by snowfall levels. As a result of analysis, the change of capacity depending on snowfall levels decreased 13.2% in case of light snow compared to a good weather, 18.6% in case of medium snow and 32.0% in case of heavy snow, so the capacity reduction rate increased as the snowfall level increased. The worsening weather appeared to have a very big possibility to act as a factor to reduce the operational efficiency of a road, so a road design and operation method considering this should be presented in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.15-26
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• 2012

Government agencies including the national police agency have executed diverse efforts including continuous improvements of traffic facilities and operation methods, education, enforcements in order to improve traffic operation systems; nevertheless there have been continuous criticisms on irrationality in traffic signal and road facility operation. One of the reasons may be the lack of systematic preliminary evaluations on various alternatives. However, there was no appropriate tool to evaluate the mobility and safety of thus alternatives in a systematic way. Therefore, this study proposes the systematic use of microscopic traffic simulation models as a comprehensive evaluation tool. In addition, this study verified the potential of using a microscopic traffic simulation model using the case of operating an overlap phase on a shared-left-turn lane through a systematic way where the evaluation was conducted through data collection, building networks, calibrating microscopic simulation models, producing performance measures, evaluating mobility and safety, and so on. As a result, the operation of overlap phase on a shared-left-turn lane showed no big difference from other operation scenarios such as leading left-turn on exclusive left turn lane in terms of mobility. However the operation of overlap phase on a shared-left-turn lane decreased safety by increasing potential conflicts.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.33-39
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• 2020

In order to cope with traffic accidents efficiently, the maximum number of traffic accidents, deaths and serious injuries that can occur during the day should be presented quantitatively. In order to examine the characteristics of traffic accidents in different regions, it was divided into the Seoul metropolitan area, Chungcheong area, Gyeongbuk area, Honam area, and Gyeongnam area and was suitable for the generalized extreme value distribution (GEV). The parameters of the GEV distribution were estimated by the L-moments, and the Anderson-Darling test and the Cramer-von Mises test confirmed the suitability of the distribution. According to the analysis, the maximum number of traffic accidents that can occur once every 50 years is 401 in the Seoul metropolitan area, 168 in the South Gyeongsang region, 455 in the North Gyeongsang region, 136 in the Chungcheong region and 205 in the South Jeolla region. Compared to the Seoul metropolitan area, which has a large population and car registration, the number of traffic accidents is relatively high due to the large area, mountainous areas, and logistics movement caused by the industrial complex.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.67-78
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• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.