• International Journal of Railway
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• v.5 no.4
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• pp.139-143
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• 2012

In Korea, almost 700 industrial parks are under operation. Generally, industrial parks consist of national industrial parks and local industrial parks which are managed by a central government and by local governments respectively. The developing countries such as Korea, China and Vietnam etc. have constructed many industrial parks, which result in the change of land use pattern and also affect future trip demands. Therefore, in estimating traffic demands, it is very important to consider the industrial park development. This study aims to improve the methodology in estimating a freight trip generation rate with the data based on a nationwide commodity freight survey. The result showed that it is desirable to apply freight trip generation rate by the industry sector in estimating freight trip generations and using the production area of firm as an indicator. Specially, the reliability of the rates through a survey could be made sure because a sample rate based on firms in industrial parks was over 25% and the response rate was over 67%. The sample rate and response rate are very superior as compared to surveys conducted in many other countries. Because industrial parks have significant effects on forecasting transportation demand in pre-feasibility studies of transport and logistics projects, it is expected that the accuracy of freight trip demands would be improved through the results of this study.

• Journal of Digital Contents Society
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• v.8 no.1
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• pp.77-83
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• 2007

The handoff area is the region where a call can be handled by the base station in any of the adjacent cells, and duration time is viewed as the time a mobile station resides in the handoff area. In this paper, probability distribution about hand off time one of important parameter at traffic modelling in mobile communication system was estimated. First, point statistic is applied using sample data obtained from simulation result to choose the group of distribution of handoff duration time. Second, parameters are estimated to decide specific distribution function. For this, the value of parameters is calculated using MLE(Maximum Likelihood Estimate.) and goodness of fit test is performed. finally these results show that handoff duration time follows gamma distribution.

• Journal of the Korean Society of Safety
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• v.10 no.2
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• pp.105-112
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• 1995

The concentrations of $NO_2$ were measured to estimate a new developed long term exposure $NO_2$ sampler at Pusan City considering the meteorological factors from May to July, in 1994. The monthly mean concentration of $NO_2$ was 15.9 PPb and 36.2 ${\mu}\ell$/day $NO_2$ and their high values were shown in the downtown of Pusan City which show potential pollution due to the increase of traffic activities and micrometeorological factors In and around the City. As a result of statistical correlation between $NO_2$ concentration by new sampling method and $NO_2$ concentration by the handy air sampler and chemiluminescent nitrogen oxides analyzer, 1994, correlation coefficients were high(r=0.93) and showed more than 0.88 value in the high concentration data set, which was arbitrarily divided into 30${\mu}\ell$ $NO_2$ concentration in an attempt to further investigate these relationships. Therefore, use of new developed long term exposure $NO_2$ sampler is good for $NO_2$ measurement and valuable for estimation of air quality in the urban and Industrial area.

• Journal of the Korean Society of Safety
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• v.26 no.4
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• pp.127-134
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• 2011

A traffic accident which happens in Expressway during dense fog is more likely to cause the sequential accidents and high death rate. So, the preventive measures shall be taken at dangerous areas to enhance the efficiency of roads and minimize the accidents and the resultant damages. So, it is necessary to find out the characteristics of freeway zone which has high risk of fog occurrence and to establish the comprehensive safety strategy on installation and operation of the safety equipment. In this study, I developed a fog forecasting model by using the freeway fog data. This model can be used as the fog forecasting model in dealing with fog problems when new road is planned. The model was developed by using a statistical analysis technique or the regression analysis, focusing on the variables such as geographical features and regional conditions, distances to water sources and the area of water source. I have segmented the models by classifying the area into inland area and coastal area. The distance to water source and area of the water source located around the freeway were found to be main factors causing fog.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1979-1990
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• 2021

Evacuation Time Estimate (ETE) can provide decision-makers with a likelihood to implement evacuation of a population with radiation exposure risk by a nuclear power plant. Thus, the ETE is essential for developing an emergency response preparedness. However, studies on ETE have not been conducted adequately in Korea to date. In this study, different cohorts were selected based on assumptions. Existing local data were collected to construct a multi-model network by TSIS-CORSIM code. Furthermore, several links were aggregated to make simple calculations, and post-processing was conducted for dealing with the stochastic property of TSIS-CORSIM. The average speed of each cohort was calculated by the link aggregation and post-processing, and the evacuation time was estimated. As a result, the average cohort-based evacuation time was estimated as 2.4-6.8 h, and the average clearance time from ten simulations in 26 km was calculated as 27.3 h. Through this study, uncertainty factors to ETE results, such as classifying cohorts, degree of model complexity, traffic volume outside of the network, were identified. Various studies related to these factors will be needed to improve ETE's methodology and obtain the reliability of ETE results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2736-2750
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• 2022

As demands for the maritime communications increase, a variety of functions and information are required to exchange via elements of maritime systems, which leads communication traffic increases in maritime frequency bands, especially in VHF (Very High Frequency) band. Thus, effective resource management is crucial to the future maritime communication systems not only to the typical terrestrial communication systems. VHF data exchange system (VDES) enables to utilize more flexible configuration according to the communication condition. This paper focuses on the VDES communication system among VDES terminals such as shore stations, ship stations and aids to navigation (AtoN) to address efficient resource allocation. We propose a resource management method considering a MIMO (Multiple Input Multiple Output) technique in VDES, which has been widely used for modern terrestrial wireless networks but not for marine environments by scheduling the essential communication resources. We introduce the general channel model in marine environment and give two metrics, spectral and the energy efficiencies to examine our resource scheduling algorithm. Based on the simulation results and analysis, the proposed method provides a possibility to enhance spectral and energy efficiencies. Additionally, we present a trade-off relationship between spectral and energy efficiencies. Furthermore, we examine the resource efficiencies related to the imperfect channel estimation.

• Smart Structures and Systems
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• v.32 no.1
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• pp.49-59
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• 2023

A bridge transportation network supplies products from various source nodes to destination nodes through bridge structures in a target region. However, recent frequent earthquakes have caused damage to bridge structures, resulting in extreme direct damage to the target area as well as indirect damage to other lifeline structures. Therefore, in this study, a surrogate model-based comprehensive framework to estimate the seismic resilience of bridge transportation networks is proposed. For this purpose, total system travel time (TSTT) is introduced for accurate performance indicator of the bridge transportation network, and an artificial neural network (ANN)-based surrogate model is constructed to reduce traffic analysis time for high-dimensional TSTT computation. The proposed framework includes procedures for constructing an ANN-based surrogate model to accelerate network performance computation, as well as conventional procedures such as direct Monte Carlo simulation (MCS) calculation and bridge restoration calculation. To demonstrate the proposed framework, Pohang bridge transportation network is reconstructed based on geographic information system (GIS) data, and an ANN model is constructed with the damage states of the transportation network and TSTT using the representative earthquake epicenter in the target area. For obtaining the seismic resilience curve of the Pohang region, five epicenters are considered, with earthquake magnitudes 6.0 to 8.0, and the direct and indirect damages of the bridge transportation network are evaluated. Thus, it is concluded that the proposed surrogate model-based framework can efficiently evaluate the seismic resilience of a high-dimensional bridge transportation network, and also it can be used for decision-making to minimize damage.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.63-73
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• 2015

In the end to end data transfer protocols, it is very important to correctly estimate available bandwidth. In UDT (UDP based Data Transfer), receiver estimates the MTR (Maximum Transfer Rate) of the current link using pair packets transmitted periodically from sender and, then sender finally decides the MTR through EWMA (Exponential Weighted Moving Average) algorithm. Here, MTR has to be exactly estimated because available bandwidth is calculated with difference of MTR and current transfer rate. However, when network is congested due to traffic load and where competing flows are coexisted, it bring about a severe fairness problem. This paper proposes a congestion degree based MTR estimation algorithm. Here, the congestion degree stands a relative index for current congestion status on bottleneck link, which is calculated with arriving intervals of a pair packets. The algorithm try to more classify depending on the congestion degree to estimate more actual available bandwidth. With the network simulation results, our proposed method showed that the fairness problem among the competing flows is significantly resolved in comparison with that of UDT.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.22-31
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• 2015

Recently, 3rd Generation Partnership Project (3GPP) has developed device-to-device (D2D) communication to cope with the explosively increasing mobile data traffic. The D2D communication uses sidelink based on single carrier-frequency division multiple access (SC-FMDA) due to its low peak-to-average power ratio (PAPR). In addition, demodulation reference signal (DMRS) is designed to support multiple input multiple output (MIMO). In this paper, we propose the DFT-based channel estimation scheme for sidelink in D2D communication. The proposed scheme uses the 2-Dimensional Minimum Mean Square Error (2-D MMSE) interpolation scheme for the user moving at a high speed. We perform the system level simulation based on 20MHz bandwidth of 3GPP LTE-Advanced system. Simulation results show that the proposed channel estimation scheme can improve signal-to-interference-plus-noise ratio (SINR), throughput and spectral efficiency of conventional scheme.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.43-50
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• 2011

The monitoring system for link travel speed using taxi probe is one of key sub-systems of ITS. Link travel speed collected by taxi probe has been widely employed for both monitoring the traffic states of urban road network and providing real-time travel time information. When sample size of taxi probe is small and link travel time is longer than a length of time interval to collect travel speed data, and in turn the missing state is inevitable. Under this missing state, link travel speed data is real-timely not collected. This missing state changes from single to multiple time intervals. Existing single interval prediction techniques can not generate multiple future states. For this reason, it is necessary to replace multiple missing states with the estimations generated by multi-interval prediction method. In this study, a multi-interval prediction method to generate the speed estimations of single and multiple future time step is introduced overcoming the shortcomings of short-term techniques. The model is developed based on Non-Parametric Regression (NPR), and outperformed single-interval prediction methods in terms of prediction accuracy in spite of multi-interval prediction scheme.