• Journal of KIISE:Information Networking
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• v.27 no.2
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• pp.197-205
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• 2000

This paper proposes and evaluates a scheme which enhances real-time performance on the Ethernet-based residential network where cable length is very short and most of traffic passes through a specific device. The scheme aims at enhancing deadline meet ratio of the real-time traffic, for example, video stream by means of applying the functionalities such as queue discrimination and early packet discard as well as maintaining network load properly. The design includes the description on hardware interfaces along with the software modules such as scheduler, device driver and connection manager. The simulation results via SMPL suggest that the proposed network can be used as a cost-effective residential network. We also measure the permissible bandwidth for non-real-time traffic without degrading the performance of real-time traffic.

• Journal of Digital Convergence
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• v.11 no.7
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• pp.149-155
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• 2013

Recently, automobile traffic systems are evolving toward Intelligent Traffic Systems(ITS) with smart sensor technology and collaboration between traffic systems and external ones. On the contrary, Korean ITS services have little improvement comparing with other communication services. The main causes of this phenomenon are due to already allocation of ITS recommended frequency band to broadcasting services, and also dedication of DSRC frequency band over ISM band resulting in interferences. This paper provides the analysis of current Korean ITS technology status, and two suggestions to activate the ITS industry. First, need to fix the ITS standard combining WAVE and DSRC specifications soon. Second, ITS frequency allocation to complete its standard implementation in near time frame.

• The Journal of Korean Medicine
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• v.42 no.3
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• pp.72-85
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• 2021

Objectives: The objective of this study was to investigate the correlation of the scores on the State-Trait Anxiety Inventory-I (STAI-I), State-Trait Anxiety Inventory-II (STAI-II), Beck's Depression Inventory (BDI), and Core Seven Emotions Inventory-short form (CSEI-s) scales with pain improvement. Methods: We retrospectively investigated the medical records of 66 traffic accident inpatients who satisfy the selection criteria. They had received Korean medical treatment including acupuncture, electroacupuncture, pharmacopuncture, herbal medicine, and Chuna during hospitalization. STAI-II, BDI, and CSEI-s scores on hospital day 1, and STAI-I and numerical rating scale(NRS) scores on hospital day 1, 4, 7, and 10 were used for analysis. Pain improvement was evaluated by difference in NRS scores between hospital day 1 and hospital day 4, 7, 10. Results: The STAI-I, BDI, and CSEI-s scores showed significant correlations with pain or pain improvement. Conclusions: This study may be used in the research on psychological state and pain management of traffic accident patients and for patient education. Large-scale, well-designed studies need to be conducted in future to strengthen the results in this regard.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Journal of Navigation and Port Research
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• v.38 no.1
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• pp.65-71
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• 2014

Gyeongbu and Namhae expressways in the country, are the major arterial highways which are connected with the Busan port in the north-south and east-west directions, respectively, and required to study the traffic characteristics about the hourly volume factors(K-factor) by concentrated midium-size and large-size cargo trucks of 20% or higher in expressways. We therefore attempted to predict the K-factor in expressways through the correlation analysis between K-factor and K-factor estimates on the basis of the short-term VDS data collected at the basic segments of the above major expressways. As a result, power model appeared to be appropriate in predicting K-factor by the K-factor estimate based on VDS data for 7 days with a high explanatory power and validity.

• Journal of Environmental Science International
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• v.22 no.9
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• pp.1199-1211
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• 2013

Mobile sources produce a significant fraction of total anthropogenic emissions in Korea and have harmful effects on air quality. Mobile emissions are intrinsically difficult to estimate due to complicated road networks and variations of traffic volume with location and time. To measure traffic pollutants with high temporal and spatial resolution under real conditions a mobile laboratory was designed. The mobile laboratory provide concentrations of $SO_2$, CO, NO, $NO_2$ and location coordinate value. This approach allowed for pollutant level measurements on many roads within short periods of time. In this study, on-road concentrations of $SO_2$, CO, NO and $NO_2$ were measured using mobile platform measurement along the 25 main roads in Busan to estimate the average air pollution level in short time difference. The measurements were conducted on favorable meteorological days from 2010 to 2012 and the overall concentrations of $SO_2$, CO, NO and $NO_2$ were 0.006, 0.8, 0.182 and 0.055 ppm respectively. The result showed that the concentration of CO, NO and $NO_2$ on road were twice, 18 times and 2.5 times higher than regional air quality monitoring sites mean in same period.

• Journal of Environmental Health Sciences
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• v.49 no.3
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• pp.149-158
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• 2023

Background: Injury is one of the major health problems in South Korea. Few studies have evaluated both intentional and unintentional injury when investigating the association between exposure to air pollutants and injury. Objectives: We aimed to explore the association between short-term exposure to ambient air pollution and years of life lost (YLLs) due to injury. Methods: Data on daily YLLs for 2002~2019 were obtained from the the Death Statistics Database of the Korean National Statistical Office. This study estimated short-term exposure to particulate matter with an aerodynamic diameter of <10 ㎛ (PM₁₀), particulate matter with an aerodynamic diameter of <2.5 ㎛ (PM_2.5), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃). This time series study was conducted using a generalized additive model (GAM) assuming a Gaussian distribution. We also evaluated a delayed effect of ambient air pollution by constructing a lag structure up to seven days. The best-fitting lag was selected based on smallest generalized cross validation (GCV) value. To explore effect modification by intentionality of injury (i.e., intentional injury [self-harm, assault] and unintentional injury), we conducted stratified subgroup analyses. Additionally, we stratified unintentional injury by mechanism (traffic accident, fall, etc.). Results: During the study period, the average daily YLLs due to injury was 307.5 years. In the intentional injury, YLLs due to self-harm and assault showed positive association with air pollutants. In the unintentional injury, YLLs due to fall, electric current, fire and poisoning showed positive association with air pollutants, whereas YLLs due to traffic accident, mechanical force and drowning/submersion showed negative associations with air pollutants. Conclusions: Injury is recognized as preventable, and effective strategies to create a safe society are important. Therefore, we need to establish strategies to prevent injury and consider air pollutants in this regard.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.83-95
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• 2003

This study aims to develop travel time estimation and prediction models on the freeway using measurements from vehicle detectors. In this study, we established a travel time estimation model using traffic volume which is a principle factor of traffic flow changes by reviewing existing travel time estimation techniques. As a result of goodness of fit test. in the normal traffic condition over 70km/h, RMSEP(Root Mean Square Error Proportion) from travel speed is lower than the proposed model, but the proposed model produce more reliable travel times than the other one in the congestion. Therefore in cases of congestion the model uses the method of calculating the delay time from excess link volumes from the in- and outflow and the vehicle speeds from detectors in the traffic situation at a speed of over 70km/h. We also conducted short term prediction of Kalman Filtering to forecast traffic condition and more accurate travel times using statistical model The results of evaluation showed that the lag time occurred between predicted travel time and estimated travel time but the RMSEP values of predicted travel time to observations are as 1ow as that of estimation.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.107-116
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• 2005

The information on travel time in providing the information of traffic to drivers is one of the most important data to control a traffic congestion efficiently. Especially, this information is the major element of route choice of drivers, and based on the premise that it has the high degree of confidence in real situation. This study developed a vehicle arrival time prediction algorithm called as "VAT-DV" for 6 corridors in total 6.1Km of "Nam-san area trffic information system" in order to give an information of congestion to drivers using VMS, ARS, and WEB. The spatial scope of this study is 2.5km~3km sections of each corridor, but there are various situations of traffic flow in a short period because they have signalized intersections in a departure point and an arrival point of each corridor, so they have almost characteristics of interrupted and uninterrupted traffic flow. The algorithm uses the information on a demand volume and a queue length. The demand volume is estimated from density of each points based on the Greenburg model, and the queue length is from the density and speed of each point. In order to settle the variation of the unit time, the result of this algorithm is strategically regulated by importing the AVI(Automatic Vehicle Identification), one of the number plate matching methods. In this study, the AVI travel time information is composed by Hybrid Model in order to use it as the basic parameter to make one travel time in a day using ILD to classify the characteristics of the traffic flow along the queue length. According to the result of this study, in congestion situation, this algorithm has about more than 84% degree of accuracy. Specially, the result of providing the information of "Nam-san area traffic information system" shows that 72.6% of drivers are available.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2106-2111
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• 2003

In this paper we discuss an algorithm for a real time transmission of moving color images on the TCP/IP network using wavelet transform and neural network. The image frames received from the camera are two-level wavelet-trans formed in the server, and are transmitted to the client on the network. Then, the client performs the inverse wavelet-transform using only the received pieces of each image frame within the prescribed time limit to display the moving images. When the TCP/IP network is busy, only a fraction of each image frame will be delivered. When the line is free, the whole frame of each image will be transferred to the client. The receiver warns the sender of the condition of traffic congestion in the network by sending a special short frame for this specific purpose. The sender can respond to this information of warning by simply reducing the data rate which is adjusted by a back-propagation neural network. In this way we can send a stream of moving images adaptively adjusting to the network traffic condition.