• The Korean Journal of Ceramics
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• v.4 no.4
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• pp.368-371
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• 1998

The work aims to evaluate the necessary physical properties of Abrasion Resistant Pavers designed for high volume pedestrian and road vehicle traffic and their influence on the selection of raw materials and ceramic processes. The pavers' specifications such as high strength and ware resistance demand a careful clay preparation, slow drying, slow firing and a balanced chemical and mineralogical composition. Therefore, developing abrasion Resistant Pavers in existing modern brickmaking plants, which are designed primarily for making bricks and pavers for domestic applications, has become a challenge for manufacturers and ceramic professionals. The significance of quality control and research and development in the production of these high class pavers is also emphasised in this work through the investigation of a paver that exhibits shrinkage cracking.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.145-157
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• 2004

The cycle length design model of the Korean traffic responsive signal control systems is devised to vary a cycle length as a response to changes in traffic demand in real time by utilizing parameters specified by a system operator and such field information as degrees of saturation of through phases. Since no explicit guideline is provided to a system operator, the system tends to include ambiguity in terms of the system optimization. In addition, the cycle lengths produced by the existing model have yet been verified if they are comparable to the ones minimizing delay. This paper presents the studies conducted (1) to find shortcomings embedded in the existing model by comparing the cycle lengths produced by the model against the ones minimizing delay and (2) to propose a new direction to design a cycle length minimizing delay and excluding such operator oriented parameters. It was found from the study that the cycle lengths from the existing model fail to minimize delay and promote intersection operational conditions to be unsatisfied when traffic volume is low, due to the feature of the changed target operational volume-to-capacity ratio embedded in the model. The 64 different neural network based cycle length design models were developed based on simulation data surrogating field data. The CORSIM optimal cycle lengths minimizing delay were found through the COST software developed for the study. COST searches for the CORSIM optimal cycle length minimizing delay with a heuristic searching method, a hybrid genetic algorithm. Among 64 models, the best one producing cycle lengths close enough to the optimal was selected through statistical tests. It was found from the verification test that the best model designs a cycle length as similar pattern to the ones minimizing delay. The cycle lengths from the proposed model are comparable to the ones from TRANSYT-7F.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.155-166
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• 2002

Since 1990s. there has been an increasing number of traffic accidents at intersection. which requires more urgent measures to insure safety on intersection. This study set out to analyze the road conditions, traffic conditions and traffic operation conditions on signalized intersection. to identify the elements that would impose obstructions in safety, and to develop a traffic accident prediction model to evaluate the safety of an intersection using the cop relation between the elements and an accident. In addition, the focus was made on suggesting appropriate traffic safety policies by dealing with the danger elements in advance and on enhancing the safety on the intersection in developing a traffic accident prediction model fir a signalized intersection. The data for the study was collected at an intersection located in Wonju city from January to December 2001. It consisted of the number of accidents, the road conditions, the traffic conditions, and the traffic operation conditions at the intersection. The collected data was first statistically analyzed and then the results identified the elements that had close correlations with accidents. They included the area pattern, the use of land, the bus stopping activities, the parking and stopping activities on the road, the total volume, the turning volume, the number of lanes, the width of the road, the intersection area, the cycle, the sight distance, and the turning radius. These elements were used in the second correlation analysis. The significant level was 95% or higher in all of them. There were few correlations between independent variables. The variables that affected the accident rate were the number of lanes, the turning radius, the sight distance and the cycle, which were used to develop a traffic accident prediction model formula considering their distribution. The model formula was compared with a general linear regression model in accuracy. In addition, the statistics of domestic accidents were investigated to analyze the distribution of the accidents and to classify intersections according to the risk level. Finally, the results were applied to the Spearman-rank correlation coefficient to see if the model was appropriate. As a result, the coefficient of determination was highly significant with the value of 0.985 and the ranks among the intersections according to the risk level were appropriate too. The actual number of accidents and the predicted ones were compared in terms of the risk level and they were about the same in the risk level for 80% of the intersections.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.25-40
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• 2022

This study is about how the quality of M2M big data that provides real-time information affects users. Recently, there are many difficulties in acquiring and managing data because data types such as variety, data volume, and data velocity are changing rapidly and diversified. This not only leads to a decrease in data quality but also it can give a negative impact when making decisions using data. Generally, the quality of data is defined as 'suitability for use', which means that data quality must meet the expectations of user needs. Therefore, data providers need activities to improve data quality for this purpose, and the key is to identify data quality dimensions in each field where data is used and provide data suitable for the level of user needs. In this study, the relationship between the quality area of real-time M2M data used in the traffic information system and user satisfaction was analyzed. Research models and hypotheses were established to analyze the effects between variables related to M2M big data. In order to test the hypothesis, a causal relationship between the major factors was identified by conducting a survey and analyzing the data users.

• International Journal of Highway Engineering
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• v.18 no.4
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• pp.19-28
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• 2016

PURPOSES : Long-life asphalt pavements are used widely in developed countries. In order to be able to devise an effective maintenance strategy for such pavements, in this study, we evaluated the performance of the long-life asphalt pavements constructed along the national highways in South Korea. Further, an economic evaluation of the long-life asphalt pavements was performed based on a life-cycle cost analysis. We aimed to devise a model for evaluating the performance of long-life asphalt pavements using the national highway pavement management system (PMS) database as well as for analyzing the economic feasibility of such pavements, in order to promote their use in South Korea. METHODS : The maintenance history and pavement performance data were obtained from the national highway PMS database. The pavement performances for a total of 292 sections of 10 lanes (5 northbound lanes and 5 eastbound lanes) of national highways were used in this study. Models to predict the performances of hot mix asphalt (HMA) and long-life asphalt pavements under two distinct traffic conditions were developed using a simple regression method. Further, the economic feasibility of long-life asphalt pavements was evaluated using the Korea Pavement Management System (KoPMS). RESULTS : We developed service-life prediction models based on the traffic volume and the equivalent of single-axle load and found that long-life asphalt pavements have service lives 50% longer than those of HMA pavements. Further, the results of the economic analysis showed that long-life asphalt pavements are superior in terms of various economic indexes, including user cost, delay cost, total cost, and user benefits, even though their maintenance cost is higher than that of HMA pavements. A comparison of the economic feasibilities of the various groups showed that group A is superior to HMA pavements in all aspects except in terms of the maintenance criterion (crack 20% or higher) as per the NPV index. However, the long-life asphalt pavements in group B were superior in terms of the maintenance criterion (crack 25% or higher) regardless of the economic feasibility. CONCLUSIONS : The service life of long-life asphalt pavements was found to be approximately 50% longer than that of HMA pavements, regardless of the traffic volume characteristics. The economic feasibility of long-life asphalt pavements was evaluated based on the KoPMS. The results of the economic analysis were the following: long-life asphalt pavements are exceptional in terms of almost all factors, such as user cost, delay cost, total cost, and user benefit; however, the exception is the maintenance cost. Further, the economic feasibility of the long-life asphalt pavements in group B was found to be better than that of the HMA pavements (crack 25% or higher).

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.79-88
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• 2009

The objective of this study is to develop the new vehicle classification algorithm and minimize classification errors. The existing vehicle classification algorithm collects data from loop and piezo sensors according to the specification("Vehicle classification guide for traffic volume survey" 2006) given by the Ministry of Land, Transport and Maritime Affairs. The new vehicle classification system collects the vehicle length, distance between axles, axle type, wheel-base and tire type to minimize classification error. The main difference of new system is the "Wandering" sensor which is capable of measuring the wheel-base and tire type(single or dual). The wandering sensor obtains the wheel-base and tire type by detecting both left and right tire imprint. Verification tests were completed with the total traffic volume of 762,420 vehicles in a month for the new vehicle classification algorithm. Among them, 47 vehicles(0.006%) were not classified within 12 vehicle types. This results proves very high level of classification accuracy for the new system. Using the new vehicle classification algorithm will improve the accuracy and it can be broadly applicable to the road planning, design, and management. It can also upgrade the level of traffic research for the road and transportation infrastructure.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.483-490
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• 2019

In the Network Intrusion Detection System (NIDS), the function of classification is very important, and detection performance depends on various features. Recently, a lot of research has been carried out on deep learning, but network intrusion detection system experience slowing down problems due to the large volume of traffic and a high dimensional features. Therefore, we do not use deep learning as a classification, but as a preprocessing process for feature extraction and propose a research method from which classifications can be made based on extracted features. A stacked AutoEncoder, which is a representative unsupervised learning of deep learning, is used to extract features and classifications using the Random Forest classification algorithm. Using the data collected in the IOT environment, the performance was more than 99% when normal and attack traffic are classified into multiclass, and the performance and detection rate were superior even when compared with other models such as AE-RF and Single-RF.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.6
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• pp.90-101
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• 2016

This study was initiated to develop an algorithm for traffic condition adaptive optimal traffic signal control for isolated intersections based on the vehicle trajectory data. The algorithm determines the optimal cycle length, phase lengths, phase sequences using the data collected under V2X communication environment every second. In addition, the algorithm utilizes a traditional feature of the actuated signal control, gap-out, using traditional detector systems to consider the mixture of normal vehicles and vehicles equipped with the V2X communication function. The performance of the algorithm was compared with that of the fixed signal timing plan which was optimized with Synchro under a microscopic traffic simulation-based test bed. As a result, the overall performance, including average delay, average stop delay, the number of stops, and average speed, are improved apparently. In addition, the amount of improvement get bigger as the traffic volume in the intersection as well as the number of vehicles equipped with the V2X communication function increase.

• Journal of Information Technology and Architecture
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• v.11 no.4
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• pp.449-462
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• 2014

In big data era, there are a number of considerable parts in processing systems for capturing, storing, and analyzing stored or streaming data. Unlike traditional data handling systems, a big data processing system needs to concern the characteristics (format, velocity, and volume) of being handled data in the system. In this situation, virtualized computing platform is an emerging platform for handling big data effectively, since virtualization technology enables to manage computing resources dynamically and elastically with minimum efforts. In this paper, we analyze resource utilization of virtualized computing resources to discover suitable deployment models in Apache Hadoop and HBase-based big data processing environment. Consequently, Task Tracker service shows high CPU utilization and high Disk I/O overhead during MapReduce phases. Moreover, HRegion service indicates high network resource consumption for transfer the traffic data from DataNode to Task Tracker. DataNode shows high memory resource utilization and Disk I/O overhead for reading stored data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2D
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• pp.137-145
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• 2012

According to the National Police Agency, the total number of traffic accidents which occurred in 2010 was 226,878. Intersection accidents accounts for 44.8%, the largest portion of the entire traffic accidents. An research on the signalized intersection is constantly made, while an research on the unsignalized intersection is yet insufficient. This study selected traffic volume, road width, and sight distance as the input variables which affect unsignalized intersection accidents, and number of accidents as the output variable to build a model using ANFIS(Adaptive Neuro-Fuzzy Inference System). The forecast performance of this model is evaluated by comparing the actual measurement value with the forecasted value. The compatibility is evaluated by R2, the coefficient of determination, along with Mean Absolute Error (MAE) and Mean Square Error (MSE), the indicators which represent the degree of error and distribution. The result shows that the $R^2$ is 0.9817, while MAE and MSE are 0.4773 and 0.3037 respectively, which means that the explanatory power of the model is quite decent. This study is expected to provide the basic data for establishment of safety measure for unsignalized intersection and the improvement of traffic accidents.