• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.83-99
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• 2021

With the advent of the fourth industrial revolution, studies on driving management and driving strategies of autonomous vehicles are emerging. While obtaining microscopic traffic data on vehicles is essential for such research, we also see that conventional traffic data collection methods cannot collect the driving behavior of individual vehicles. In this study, UAV videos were used to collect traffic data from the viewpoint of the aerial base that is microscopic. To overcome the limitations of the related research in the literature, the micro-traffic data were estimated using the multiple object tracking of deep learning and an image registration technique. As a result, the speed obtained error rates of MAE 3.49 km/h, RMSE 4.43 km/h, and MAPE 5.18 km/h, and the traffic obtained a precision of 98.07% and a recall of 97.86%.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.4
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• pp.348-356
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• 2014

To understand the relationship between road-traffic noise and urban components such as population, building, road-traffic and land-use, the city of Cheongju that already has road-traffic noise maps of daytime and nighttime was selected for this study. The whole area of the city is divided into square cells of a uniform size and for each cell, the urban components are estimated. A spatial representative noise level for each cell is determined by averaging out population-weighted facade noise levels for noise exposure population within the cell during nighttime. The relationship between the representative noise level and the urban components is statistically modeled at the cell level. Specially, we introduce a spatial auto regressive model and a spatial error model that turns out to explain above 85 % of the noise level. These findings and modeling methods can be used as a preliminary tool for environmental planning and urban design in modern cities in consideration of noise exposure.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.45-53
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• 2009

The fast progress on multimedia data acquisition technologies has enabled collecting vast amount of videos in real time. Although the amount of information gathered from these videos could be high in terms of quantity and quality, the use of the collected data is very limited typically by human-centric monitoring systems. In this paper, we propose a framework for analyzing long traffic video using series of content-based analyses tools. Our framework suggests a method to integrate theses analyses tools to extract highly informative features specific to a traffic video analysis. Our analytical framework provides (1) re-sampling tools for efficient and precise analysis, (2) foreground extraction methods for unbiased traffic flow analysis, (3) frame property analyses tools using variety of frame characteristics including brightness, entropy, Harris corners, and variance of traffic flow, and (4) a visualization tool that summarizes the entire video sequence and automatically highlight a collection of frames based on some metrics defined by semi-automated or fully automated techniques. Based on the proposed framework, we developed an automated traffic flow analysis system, and in our experiments, we show results from two example traffic videos taken from different monitoring angles.

• Spatial Information Research
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• v.22 no.1
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• pp.9-17
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• 2014

This study was intended to reliably predict the traffic green house gas emission in Daegu with the use of spatial statistical technique and calculate the traffic green house gas emission of each administrative district on the basis of the accurately predicted emission. First, with the use of the traffic actually surveyed at a traffic observation point, and traffic green house gas emission was calculated. Secondly, on the basis of the calculation, and with the use of Universal Kriging technique, this researcher set a suitable variogram modeling to accurately and reliably predict the green house gas emission at non-observation point suitable through spatial correlation, and then performed cross validation to prove the validity of the proper variogram modeling and Kriging technique. Thirdly, with the use of the validated kriging technique, traffic green gas emission was visualized, and its distribution features were analyzed to predict and calculate the traffic green house gas emission of each administrative district. As a result, regarding the traffic green house gas emission of each administration, it was found that Bukgu had the highest green house gas emission of $291,878,020kgCO_2eq/yr$.

• Journal of Korea Spatial Information System Society
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• v.2 no.2 s.4
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• pp.39-48
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• 2000

The purpose of this study is to develop a converting algorithm for providing real-time traffic information through the FM DARC(DAta Radio Channel). The converting algorithm based on the GIS node-link system enables raw traffic data to be a standard traffic information. The standard traffic information in data quality, quantity and format is an essential to construct the FM DARC traffic information system effectively. We have developed the algorithm and applied it to the FM DARC system. After introducing FM DARC briefly, this paper presents GIS DB, the converting algorithm and the client/server FM DARC system.

• Spatial Information Research
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• v.22 no.2
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• pp.89-97
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• 2014

This study investigated how much the spread of traffic control information affect the traffic congestion in order to identify the behavior of the individual drivers that impacts on the entire transport system. For this purpose, agent-based transportation model was constructed. GIS data were directly used for the transportation model and the processing steps of the simulation results are presented. The results showed that the average speed was not lowered when the traffic information was provided to 30 to 70% of total drivers. In contrast, the driver's average speed is reduced when he traffic information was provided to less than 20% or 80% or more. In summary, the provision of traffic information to drivers has an influence on the traffic flow and bypassing vehicles can generate local congestion. This results can be used as a basis for the future direction of road transport policy.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.1 s.5
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• pp.91-104
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• 1995

The spatial aggregation of data, in transportation and other planning processes, is an important theoretical consideration because the results of any analysis are not entirely independent of the delineation of zones. Moreover, using a different spatial aggregation may lead to different, and sometimes contradictory conclusions. Two criteria have been considered as important in designing zone systems. They are scale and aggregation. The scale problem arises because of uncertainty about the number of zones needed for a study and the aggregation problem arises because of uncertainty about how the data are to be aggregated to from a given scale problem. In a transportation study, especially in the design of traffic analysis zone(TAZ), the scale problem is directly related to the number dof zones and the aggregation problem involves spatial clustering, meeting the general requirements of forming the zones system such as equal traffic generation, convexity, and the consistency with the political boundary. In this study, first, the comparative study of delineating spatial units has been given. Second, a FORTRAN-based heuristic algorithm for designing TAZ based on socio-economic data has been developed and applied to the Korean peninsula containing 132 micro parcels. The vector type ARC/INFO GIS topological data mosel has been used to provise the adjacency information between parcels. The results, however, leave some to be desired in order to overcome such problems as non-convexity of the agglomerated TAZ system and/or uneven traffic phenomenon for each TAZ.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.394-396
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• 2005

IEEE 802.11를 비롯한 여러 무선 네트워크에서는 multi-rate을 활용한 시스템 성능향상에 관한 연구가 진행되고 있다. 그 중에 한가지 연구결과로 제안된 방법이 rDCF.이다. 만약 Mesh 네트워크에서 rDCF를 동작시킨다면, 시스템 throughput의 증가, Packet delay의 감소와 항께 채널상태에 따라 포워딩 전략을 다르게 함으로써 채널 error의 영향이 줄어들 것으로 기대해 볼 수 있다. 하지만 기존의 rDCF를 아무런 revision 없이 Mesh 네트워크에 적용하기에는 spatial reuse 측면에서 비효율적이다. Mesh 네트워크에서는 외부 네트워크와 access point 지점이 되는 portal쪽으로 traffic이 집중되는 것이 일반적이므로 portal에 가까울수록 traffic간의 contention도 가중되므로 시스템 전체 성능에 영향을 미치게 된다. 이러한 문제를 줄이기 위하여 무선 네트워크 환경에서 spatial reuse 측면을 향상시킴으로써 동시에 진행되는 communication 수를 늘리는 방법이 있다. 그러므로 본 논문에서 rDCF의 spatial reuse를 늘임으로써 좀더 Mesh Network위에서도 효율적으로 작동할 수 있는 기법을 제시하고자 한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6D
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• pp.1003-1010
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• 2006

This study developed a traffic accident information management system based on WebGIS that can process a lot of data for giving effectively diagnosis of traffic accidents in serious damage circumstances by traffic accident. Also, this study presents a way to compose and to convey traffic accident information. In addition, non-spatial attributes as well as spatial attributes about traffic accidents information be integrated and managed by the system. To provide Web service, we developed modules that can supply visually spatial information and traffic accidents data through ASP, Javascript, ArcIMS based on Web and constructed a server. And constructed system include a function that offer the now situation of traffic accident in real time, which supply the statistical data of traffic accident through Web as soon as user entry data in comparison with previous way that preparatory period until traffic accidents data is supplied to peoples had been long. Traffic accidents are analyzed with only nonspatial attribute by simply collecting in the past. However, system constructed by this study offer new function that can grasp visually accident spot circumstance and use detailed content and accurate location data as well as statistical data of traffic accidents. Also, it offer interface that can connect directly with accident charge policeman.