• The Journal of Korean Medicine
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• v.42 no.1
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• pp.26-45
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• 2021

Objectives: The purpose of this study was to investigate characteristics of patients who were admitted to an oriental medicine hospital by traffic accident. Methods: The medical charts of 346 patients admitted to an oriental medicine hospital from June 1, 2017 to May 31, 2018 were analyzed. The Numbering Rating Scale (NRS) and duration of hospitalization were used to evaluate characteristics of the patients. Results: Acupuncture, Moxibustion, Infralux were used to treat all the patients. The most frequently used herbal medication was Danggwisu-san(22.25%). 87 patients(25.14%) visited the outpatient department after being discharged from the hospital. The most frequent complaint in terms of pain was cervical pain(82.7%) and of systemic symptom was headache(23.7%). Men and younger aged patients showed higher therapeutic effect than women and older ages. The most common duration of hospitalization was 2~4 days(42.73%) and positively correlated with therapeutic effect. The most frequent interval between time of injury and visit to the hospital was from 0-1 days(68.90%) and showed no relationship with therapeutic effect. The most frequent admission pathway was "Directly to the hospital"(57.51%). Admission pathway was proportionally associated with duration of hospitalization and treatment results were not. The most common vehicle type involved in the traffic accidents was a sedan(72.25%), accident type was a rear-end collision(43.64%) and showed no relationship with therapeutic effect. Conclusions: In this study, therapeutic effects were highly correlated among men, younger ages, and duration of hospital stay, and was not for interval days, admission pathway, vehicle type, and accident type.

• Environmental Analysis Health and Toxicology
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• v.30
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• pp.10.1-10.14
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• 2015

Recent cohort studies have relied on exposure prediction models to estimate individual-level air pollution concentrations because individual air pollution measurements are not available for cohort locations. For such prediction models, geographic variables related to pollution sources are important inputs. We demonstrated the computation process of geographic variables mostly recorded in 2010 at regulatory air pollution monitoring sites in South Korea. On the basis of previous studies, we finalized a list of 313 geographic variables related to air pollution sources in eight categories including traffic, demographic characteristics, land use, transportation facilities, physical geography, emissions, vegetation, and altitude. We then obtained data from different sources such as the Statistics Geographic Information Service and Korean Transport Database. After integrating all available data to a single database by matching coordinate systems and converting non-spatial data to spatial data, we computed geographic variables at 294 regulatory monitoring sites in South Korea. The data integration and variable computation were performed by using ArcGIS version 10.2 (ESRI Inc., Redlands, CA, USA). For traffic, we computed the distances to the nearest roads and the sums of road lengths within different sizes of circular buffers. In addition, we calculated the numbers of residents, households, housing buildings, companies, and employees within the buffers. The percentages of areas for different types of land use compared to total areas were calculated within the buffers. For transportation facilities and physical geography, we computed the distances to the closest public transportation depots and the boundary lines. The vegetation index and altitude were estimated at a given location by using satellite data. The summary statistics of geographic variables in Seoul across monitoring sites showed different patterns between urban background and urban roadside sites. This study provided practical knowledge on the computation process of geographic variables in South Korea, which will improve air pollution prediction models and contribute to subsequent health analyses.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1575-1580
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• 2017

The electric vehicle business is important because it can reduce 30% of the fine dust generated in the metropolitan area and it can solve the air pollution problem by replacing automobile exhaust gas from an internal combustion engine with eco-friendly electric cars. For the construction of the electric charging station infrastructure, which is the core part of the electric car business, we focus to select the optimal location of the electric car charging station in Seoul. The goal of this paper is to utilize and analyze the traffic statistics of T-Map navigation users data and Seoul Metropolitan Transportation Policy Department to deploy the electric cars charging station with optimal location to increase the efficiency. In this paper, the proposed algorithm is composed of two parts of electric charging station selection. First, we analyze real traffic statistics and area. Second, we utilize T-Map navigation data distribution. To select optimal electric charging station location, we apply these two algorithms.

• Journal of the Korean Society of Marine Environment & Safety
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• v.18 no.4
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• pp.323-330
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• 2012

Manila Bay is home to the Port of Manila with three harbors: North Harbor, South Harbor and MICT(Manila International container Terminal). There is an adjacent fishing port to the north and another port across the Bay, the Limao Port. This study focuses on the volume of traffic movement in the Bay area taken from Manila VTMS raw data of the arrival and departure movements only. It is a two-year period of study of 2010 and 2011 traffic volume. It divides the data according to their numbers; to their sizes measured in gross tons; to the time of vessels' movements, whether daytime or night-time; and to each voyage trade: domestic or foreign. Quantitative values are calculated from the raw data based on the whole population of the two-year period. The results are illustrated by tables and graphs. Statistical measures are applied to determine the spread and frequencies of the data and test any significance from the hypotheses. These are shown in the tabulated form and interpreted to give a better picture of the frequency and volume of traffic. In the end, a summary is offered where it is hoped that this paper will propel further studies of improving the safety behavior in the premier port of the country.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.5
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• pp.38-48
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• 2010

Adverse weather (e.g. strong winds, snow and ice) will probably appear as a more serious and frequent threat to road traffic than in clear climate. Another consequence of climate change with a natural disastrous on road traffic is respond to traffic accident more the large and high-rise bridge zone, tunnel zone, inclined plane zone and de-icing zone than any other zone, which in turn calls for continuous adaption of monitoring procedures. Accident mitigating measures against this accident category may consist of intense winter maintenance, the use of road weather information systems for data collection and early warnings, road surveillance and traffic control. While hazard from reduced road friction due to snow and ice may be eliminated by snow removal and de-icing measures, the effect of strong winds on road traffic are not easily avoided. The purpose of the study described here, was to design of amber information the relationship between traffic safety, weather, user information on road weather and driving conditions in local-scale Geographic. The most applications are the optimization of the amber information definition, improvements to road surveillance, road weather monitoring and improved accuracy of user information delivery. Also, statistics on wind gust, surface condition, vehicle category and other relevant parameters for wind induced accidents provide basis for traffic control, early warning policies and driver education for improved road safety at bad weather-exposed locations.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.125-136
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• 2005

The current traffic accident reduction procedure in economic feasibility study does not consider the characteristics of road and V/C ratio. For solving this problem, this paper suggests methods to be able to evaluate safety of each road in construction and improvement through developing accident Prediction model in reflecting V/C ratio Per road types and traffic characters. In this paper as primary process, model is made by tke object of urban roads. Most of all, factor effecting on accident relying on road types is selected. At this point, selecting criteria chooses data obtained from road planning procedure, traffic volume, existence or non-existence of median barrier, and the number of crossing point, of connecting road. and of traffic signals. As a result of analyzing between each factor and accident. all appear to have relatives at a significant level of statistics. In this research, models are classified as 4-categorized classes according to roads and V/C ratio and each of models draws accident predicting model through Poisson regression along with verifying real situation data. The results of verifying models come out relatively satisfactory estimation against real traffic data. In this paper, traffic accident prediction is possible caused by road's physical characters by developing accident predicting model per road types resulted in V/C ratio and this result is inferred to be used on predicting accident cost when road construction and improvement are performed. Because data using this paper are limited in only province of Jeollabuk-Do, this paper has a limitation of revealing standards of all regions (nation).

• The Korean Journal of Emergency Medical Services
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• v.1 no.1
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• pp.100-111
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• 1997

This is the first Korea study to evaluate the effects od the safety of ambulance driving and the occurrence of ambulance traffic accidents and to provide basic informaion for the description of various factors to reduce the ambulance traffic accidents. The major insturment of this study were Krean Self-Analysis Driver Opinionnaire. Questionnaire contains 8 items which measure driver's opinions or attitudes : driving courtesy, emotion, traffic law, speed, vehicle conditions, the use of drugs, high-risk behaviors, and human factors. To take the analysis of data, the total of 350 divers were investigated ambulance divers and others in Taejon City and others (6 City) from 1996. 1. July to 1996. 31. July. The data were analyzed by the descriptive statistics and the logistic regression - path analysis - with SPSS and SAS package program. The result are as follows : 1. There was desirable attitude group(16.2%) and undesirable attitude group(17.6%) on safety ambulance driving. 2. It have suggested that risk factors of ambulance traffic accidents much affected with emotion and speed control on safety ambulance driving < Y(Accdient) = -2.64 + 0.57 $X_1$ (Emotion Control) + 0.30 $X_2$(Seed control) + E > and motor traffic acident much affected with emotion control and high-risk behavior on safety driving < Y(Accident) = -1.11 + 0.33 $X_1$(Emotion Control) + 0.29 $X_2$(High-risk Behvior) + E > 4. The primary emphassis of ambulance drivers was make us realized that improthatnt factors on safety ambulance driving were 1)making way for emergent ambulance, 2)driver's career, 3)The ability of emergency medical technics, and the knowledge or under standing of ambulance way difficut(or easy) of accdess. 5. Almost 96.6% of respondents have agreed to necessity of emergency medical technics for ambulance drivers. 6. Almost 94.6% of respondents have consented to necessity of emergtency medical technicians for ambulance driving. 7. It have suggested that the proportion of traffic accident proportion by desitable attitude group(16.7%) was much less than that of undesirable attitude group(30.8%) on safety ambulance driving(P < 0.05)/Ps) Accidents are unplanned, unforesen incidents which can lead to harmful or unfortunate outcomes, Collisons are not accidents, since the basic cause of the majority of collisons invovles high-risk human behavior. Although there are many factors which contribute to accident causation, four basic factors seem to predominate in most traffic related situations. These four factors include: the human factor, the vehicle factor, the environmental factors and destination factor(Peto G. et al. 1995).

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.291-305
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• 2013

Collective traffic data (BPS, PPS etc.) for detection against the distributed denial of service attack on network is the time sequencing big data. The algorithm to detect the change point in the big data should be accurate and exceed in detection time and detection capability. In this work, the sliding window and discretization method is used to detect the change point in the big data, and propose five nonparametric test statistics using empirical distribution functions and ranks. With various distribution functions and their parameters, the detection time and capability including the detection delay time and the detection ratio for five test methods are explored and discussed via monte carlo simulation and illustrative examples.

• Journal of Preventive Medicine and Public Health
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• v.54 no.1
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• pp.55-62
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• 2021

Objectives: After heart disease, brain stroke (BS) is the second most common cause of death worldwide, underscoring the importance of understanding preventable and treatable risk factors for the outcomes of BS. This study aimed to model the survival of patients with BS in the presence of competing risks. Methods: This longitudinal study was conducted on 332 patients with a definitive diagnosis of BS. Demographic characteristics and risk factors were collected by a validated checklist. Patients' mortality status was investigated by telephone follow-up to identify deaths that may be have been caused by stroke or other factors (heart disease, diabetes, high cholesterol, etc.). Data were analyzed by the Lunn-McNeil approach at alpha=0.1. Results: Older age at diagnosis (59-68 years: adjusted hazard ratio [aHR], 2.19; 90% confidence interval [CI], 1.38 to 3.48; 69-75 years: aHR, 5.04; 90% CI, 3.25 to 7.80; ≥76 years: aHR, 5.30; 90% CI, 3.40 to 8.44), having heart disease (aHR, 1.65; 90% CI, 1.23 to 2.23), oral contraceptive pill use (women only) (aHR, 0.44; 90% CI, 0.24 to 0.78) and ischemic stroke (aHR, 0.52; 90% CI, 0.36 to 0.74) were directly related to death from BS. Older age at diagnosis (59-68 years: aHR, 21.42; 90% CI, 3.52 to 130.39; 75-69 years: aHR, 16.48; 90% CI, 2.75 to 98.69; ≥76 years: aHR, 26.03; 90% CI, 4.06 to 166.93) and rural residence (aHR, 2.30; 90% CI, 1.15 to 4.60) were directly related to death from other causes. Significant risk factors were found for both causes of death. Conclusions: BS-specific and non-BS-specific mortality had different risk factors. These findings could be utilized to prescribe optimal and specific treatment.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.5
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• pp.571-580
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• 2012

Epidemiologic studies of air pollution need accurate exposure assessments at unmonitored locations. A land use regression (LUR) model has been used successfully for predicting traffic-related pollutants, although its application has been limited to Europe, North America, and a few Asian region. Therefore, we modeled traffic-related pollutants by LUR then examined whether LUR models could be constructed using a regulatory monitoring network in Metropolitan area in Korea. We used the annual-mean nitrogen dioxide ($NO_2$) in 2010 in the study area. Geographic variables that are considered to predict traffic-related pollutants were classified into four groups: road type, traffic intensity, land use, and elevation. Using geographical variables, we then constructed a model to predict the monitored levels of $NO_2$. The mean concentration of $NO_2$ was 30.71 ppb (standard deviation of 5.95) respectively. The final regression model for the $NO_2$ concentration included five independent variables. The LUR models resulted in $R^2$ of 0.59. The mean concentration of $NO_2$ of elementary schools was 34.04 ppb (standard deviation of 5.22) respectively. The present study showed that even if we used regulatory monitoring air quality data, we could estimate $NO_2$ moderately well. These analyses confirm the validity of land use regression modeling to assign exposures in epidemiological studies, and these models may be useful tools for assessing health effects of long-term exposure to traffic related pollution.