• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.1
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• pp.103-109
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• 2006

In this paper, VDS(Vehicle Dynamics Simulator) and ACS(Attitude Control Simulator) are developed and are verified using PILS(Process In-the Loop Simulation) between VDS and ACS. VDS is including the AOCS(Attitude & Orbit Control Subsystem) hardware modeling of geosynchronous satellite and consists of modulation concept. ACS performs the attitude determination using sensor data and generates the attitude control commands. In order to transfer the data between VDS and PCDU(Power Control & Distribution Unit), data acquisition boards were mounted. VDS performance is verified using HILS(Hardware In-the Loop Simulation) between VDS and PCDU.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.125-136
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• 2005

The goal of this paper is to develop freeway Origin-Destination (OD) demand estimation model using VDS and AVI data. The formulation of methodology proposed in this paper includes traffic flow technique to be able to remove the bi-level problem and optimal solution algorithm using a kalman filter algorithm. The proposed dynamic OD estimation model use ilk and off-ramp volumes collected from VDS and partial OD collected from AVI data to raise the accuracy of dynamic OD estimation. The proposed model is evaluated by using the real-time data of SOHAEAN freeway, South Korea. The result of the proposed dynamic OD estimation model based on VDS and AVI data is better than that of based on VDS data. The more AVI systems are equipped at on and off-ramp, the more excellent result of estimation accuracy is expected.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.1 s.12
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• pp.13-26
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• 2007

A VDS data in the domestic has been used within limits to real time information such as congestion management, incident management, and route guidance service. On the other hand, a VDS data in the foreign countries had been used to various objectives such as transportation policy assessment, transportation construction evaluation, franc safety improvement, and etc. The scope and method of the study is the VDS data which was installed in the uninterrupted flow such as the freeway and the interrupted flow in a diversion route of the leeway. It has investigated and analyzed the VDS as our subject to study, study objective and study methodology for each study generally classified as 1) data collection 2) data processing 3) data store and 4) data quality section. This study has investigated and analyzed the various literatures in domestic and foreign countries regarding the VDS data. And It drew the development direction of the study which is about VDS data in domestic from now.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.133-141
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• 2021

The development and demand for the system that provides users with traffic information and efficient road use have continued. also, this system provides the basic technology of the Intelligent Transport System (ITS). The most used traffic information collection tools are Vehicle detectors (VDS) and short-range wireless communication (DSRC) on express way. In order to generate reliable traffic information, it is necessary to efficiently manage and utilize the collected data as well as high-quality traffic data collection and processing technology. In this study, traffic information collection·processing·provision systems were investigated, and analyze the current status and problems of traffic information collected through VDS. Based on this, we would like to present an improved collection algorithm that utilizes the Kalman filter for vehicle information measurement of VDS data. By using the algorithm of this study, it is possible to minimize the time delay of the estimated value as well as the noise removal that inevitably occurs during measurement.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.27-40
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• 2006

The traffic information is provided, which based on the volume of traffic, speed, occupancy collected through the currently operating Vehicle Detector System(VDS). In addition to the trend in utilization fold of traffic information is increasing gradually with the applied various fields and users. Missing data in Vehicle detector data means series of data transmitted to controller without specific property. The missing data does not have a data property, so excluded at the whole data Process Hence, increasing ratio of missing data in VDS data inflicts unreliable representation of actual traffic situation. This study presented the imputation process due out which applied the methodologies that utilized adjacent stations reference and historical data utilize about missing data. Applied imputation process methodologies to VDS data or SeoHaeAn/Kyongbu Expressway, currently operation VDS, after processes at missing data ratio of an option. Imputation process held presented to per lane-30seconds-period, and morning/afternoon/daily time scope ranges classified, and analyzed an error of imputed data preparing for actual data. The analysis results, an low error occurred relatively in the results of the imputation process way that utilized a historical data compare with adjacent stations reference methods.

• International Journal of Highway Engineering
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• v.16 no.4
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• pp.127-134
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• 2014

PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.10
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• pp.62-66
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• 2011

In this study, a new RF method to extract the drain-source voltage Vds-dependent gate-bulk capacitance of deep-submicron MOSFETs is developed by determining Vds-independent gate-source overlap capacitance using measured S-parameters. The accuracy of extraction method is verified by observing good agreements between the measured and modeled S-parameters. The lateral channel doping profile in the drain region is experimentally measured using a Vds-dependent curve of the overlap and depletion length obtained from the extracted data.

• 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.

• International Journal of Highway Engineering
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• v.18 no.5
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• pp.117-125
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• 2016

PURPOSES : The density in uninterrupted traffic flow facilities plays an important role in representing the current status of traffic flow. For example, the density is used for the primary measures of effectiveness in the capacity analysis for freeway facilities. Therefore, the estimation of density has been a long and tough task for traffic engineers for a long time. This study was initiated to evaluate the performance of density values that were estimated using VDS data and two traditional methods, including a method using traffic flow theory and another method using occupancy by comparing the density values estimated using vehicular trajectory data generated from a radar detector. METHODS : In this study, a radar detector which can generate very accurate vehicular trajectory within the range of 250 m on the Joongbu expressway near to Dongseoul tollgate, where two VDS were already installed. The first task was to estimate densities using different data and methods. Thus, the density values were estimated using two traditional methods and the VDS data on the Joongbu expressway. The density values were compared with those estimated using the vehicular trajectory data in order to evaluate the quality of density estimation. Then, the relationship between the space mean speed and density were drawn using two sets of densities and speeds based on the VDS data and one set of those using the radar detector data. CONCLUSIONS : As a result, the three sets of density showed minor differences when the density values were under 20 vehicles per km per lane. However, as the density values become greater than 20 vehicles per km per lane, the three methods showed a significant difference among on another. The density using the vehicular trajectory data showed the lowest values in general. Based on the in-depth study, it was found out that the space mean speed plays a critical role in the calculation of density. The speed estimated from the VDS data was higher than that from the radar detector. In order to validate the difference in the speed data, the traffic flow models using the relationships between the space mean speed and the density were carefully examined in this study. Conclusively, the traffic flow models generated using the radar data seems to be more realistic.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.1-13
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• 2023

This study develops an optimal mobile-communication-based National Highway traffic volume estimation model using an ensemble-based machine learning algorithm. Based on information such as mobile communication data and VDS data, the LightGBM model was selected as the optimal model for estimating traffic volume. As a result of evaluating traffic volume estimation performance from 96 points where VDS was installed, MAPE was 8.49 (accuracy 91.51%). On the roads where VDS was not installed, traffic estimation accuracy was 92.6%.