• KIPS Transactions on Computer and Communication Systems
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• v.1 no.2
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• pp.109-118
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• 2012

As a result of the prevalence of smartphone, various mobile services became faster by LTE networks. Because many mobile devices are used more wireless services, heavy signaling traffic for authentication could be generated. Authentication is an important factor in wireless networks to identify devices; it is the start of wireless networks. This paper analyzes previous patterns for more effective authentication in accessing of another external networks. We propose a fast authentication scheme for minimizing of signaling cost between the authentication server and external networks. And we calculate the rate of authentication occurrence in LTE networks using mathematical modeling as well as the change of signaling cost for authentication in various network environments. Finally, we calculate the optimized number of authentication data and show the effectiveness for authentication signaling costs.

• International Journal of Highway Engineering
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• v.15 no.1
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• pp.127-134
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• 2013

PURPOSES: Speeding is often the primary contributor to fatal crashes. Surprisingly, driving behaviors are indirectly affected by personal factors such as law-abidance, risk sensitivity, and situational adaptability. This research aims to verify the effectiveness of pavement markings at reducing the speeds of high-risk drivers. The purpose of this study is to establish how drivers (including law-abiding or law-breaking, high-risk or low-risk) react to different pavement markings in a driving simulator. METHODS: The five different pavement markings were selected from markings used in other nations. The forty-two drivers were then surveyed, via questionnaires, and placed into the corresponding groups. Finally, statistical analysis was conducted to determine the extent of speed reduction for each pavement marking. RESULTS: Higher speeds were linked to the high-risk drivers. Furthermore, after analysis of the mean difference of average speeds by pavement marking, it was determined that Dragon's Teeth had the greatest speed reducing effect on these drivers. CONCLUSIONS: Perceptual countermeasures are unlikely to strongly affect high-risk drivers' perception of speed on the curves. This statistically demonstrates that Dragon's Teeth have a subtle effect on reducing speeds in the driving simulator. This study's significance lies in the improved understanding of high-risk drivers in terms of road facilities. It approaches the effects of various patterns of pavement markings for high-risk drivers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.559-569
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• 2016

This study examined flow between regions based on the spatial structure of the territory and characterized the flow patterns by analyzing regional networks via transportation connectivity. To accomplish this, transportation connectivity of the entire nation was examined using 2010 national express bus OD data from the Korea Transport Database. After the initial analysis, 2010 car OD data describing networks in seven regions (125 cities and districts), Gangwon-do, Gyeongsangnam-do, Gyeongsangbuk-do, Jeollanam-do, Jeollabuk-do, Chungcheong-do and Seoul, were analyzed to identify transportation connectivity. The results revealed that Korea has strong triangular-belt-shaped transportation connectivity that connects among metropolitan areas in the Jeolla and Gyeongsang areas. Particular zones are set by regional characteristics and functional connectivity for each zone. The results of this study will be useful as a basic material to establish development strategies and customized regional policy development, as well as balanced development.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.65-70
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• 2019

In recently, ship trajectory data consisting of ship position, speed, course, and so on can be obtained from the Automatic Identification System device with which all ships should be equipped. These data are gathered more than 2GB every day at a crowed sea port and used for analysis of ship traffic statistic and patterns. In this study, we propose a method to process ship trajectory data efficiently with distributed computing resources using MapReduce algorithm. In data preprocessing phase, ship dynamic and static data are integrated into target dataset and filtered out ship trajectory that is not of interest. In mapping phase, we convert ship's position to Geohash code, and assign Geohash and ship MMSI to key and value. In reducing phase, key-value pairs are sorted according to the same key value and counted the ship traffic number in a grid cell. To evaluate the proposed method, we implemented it and compared it with IALA waterway risk assessment program(IWRAP) in their performance. The data processing performance improve 1 to 4 times that of the existing ship trajectory analysis program.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.163-174
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• 2021

Track-bridge interaction has become an essential part in the design of bridges and rails in terms of modern railways. As a unique ballastless slab track, the longitudinal continuous slab track (LCST) or referred to as the China railway track system Type-II (CRTS II) slab track, demonstrates a complex force mechanism. Therefore, a comprehensive track-bridge interaction study between multi-span simply supported beam bridges and the LCST is presented in this work. In specific, we have developed an integrated finite element model to investigate the overall interaction effects of the LCST-bridge system subjected to the actions of temperature changes, traffic loads, and braking forces. In that place, the deformation patterns of the track and bridge, and the distributions of longitudinal forces and the interfacial shear stress are studied. Our results show that the additional rail stress has been reduced under various loads and the rail's deformation has become much smoother after the transition of the two continuous structural layers of the LCST. However, the influence of the temperature difference of bridges is significant and cannot be ignored as this action can bend the bridge like the traffic load. The uniform temperature change causes the tensile stress of the concrete track structure and further induce cracks in them. Additionally, the influences of the friction coefficient of the sliding layer and the interfacial bond characteristics on the LCST's performance are discussed. The systematic study presented in this work may have some potential impacts on the understanding of the overall mechanical behavior of the LCST-bridge system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.27-37
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• 2011

The IDS/IPS(Intrusion Detection/Prevention System) has been widely deployed to protect the internal network against internet attacks. Reducing the packet inspection time is one of the most important challenges of improving the performance of the IDS/IPS. Since the IDS/IPS needs to match multiple patterns for the incoming traffic, we may have to apply the multiple pattern matching schemes, some of which use finite automata, while the others use the shift table. In this paper, we first show that the performance of those schemes would degrade with various kinds of pattern sets and payload, and then propose a hybrid multiple pattern matching scheme which combines those two schemes. The proposed scheme is organized to guarantee an appropriate level of performance in any cases. The experimental results using real traffic show that the time required to do multiple pattern matching could be reduced effectively.

• The Journal of Bigdata
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• v.5 no.2
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• pp.85-95
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• 2020

In this study, a mobility analysis method is suggested to estimate an O/D trip demand estimation using Mobile Phone Signaling Data. Using mobile data based on mobile base station location information, a trip chain database was established for each person and daily traffic patterns were analyzed. In addition, a new algorithm was developed to determine the traffic characteristics of their mobilities. To correct the ping pong handover problem of communication data itself, the methodology was developed and the criteria for stay time was set to distinguish pass by between stay within the influence area. The big-data based method is applied to analyze the mobility pattern in inter-regional trip and intra-regional trip in both of an urban area and a rural city. When comparing it with the results with traditional methods, it seems that the new methodology has a possibility to be applied to the national survey projects in the future.

• The Journal of Information Systems
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• v.31 no.3
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• pp.47-65
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• 2022

Purpose With the recent development of Big Data and Artificial Intelligence technology, self-driving technology has developed into three stages (partial self-driving) or four stages (conditional self-driving), it is expected to bring a new paradigm to transportation in the city. Although many researchers are researching related technologies, there is no research on self-driving for disabled persons. In this study, the basic research was conducted based on the assumption that the shared self-driving car used by the disabled person is similar to the special transportation currently driving. Design In this study, data analysis and machine learning techniques were utilized to analyze the mobility patterns of disabled persons by type and to search for leading factors affecting the traffic volume of special transportation. Findings The study found that external physical disorders and developmental disorders often visit general welfare centers, internal organ disorders often visit general hospitals, and the elderly and mental disorders have various destinations. In addition, machine learning analysis showed that the main transportation routes for the disabled person use arterial roads and auxiliary arterial roads and that the ratio of building usage-related variables affecting the use of special transportation for a disabled person is high. In addition, the distance to the subway and bus stops was also mentioned as a meaningful variable. Based on these analysis results, it is expected that the necessary infrastructure for shared self-driving cars for disability person traffic will be used as meaningful research data in the future.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.193-204
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• 2022

Smart card data is representative mobility data and can be used for policy development by analyzing public transportation usage behavior. This paper deals with the problem of classifying metro stations using metro usage patterns as one of these studies. Since the previous papers dealing with clustering of metro stations only considered traffic among usage behaviors, this paper proposes clustering considering traffic time as one of the complementary methods. Passengers at each station were classified into passengers arriving at work time, arriving at quitting time, leaving at work time, and leaving at quitting time, and then the estimated shape parameter was defined as the characteristic value of the station by modeling each transit time to Weibull distribution. And the characteristic vectors were clustered using the K-means clustering technique. As a result of the experiment, it was observed that station clustering considering pass time is not only similar to the clustering results of previous studies, but also enables more granular clustering.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.95-103
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• 2024

With the rapid decline of the school-age population, the competition for admissions has increased and marketing through digital channels has become more important, so universities are investing more resources in online promotion and communication to recruit new students. This study uses Google Analytics, a web log analysis tool, to track the visitor behavior of a university admissions website and establish a digital marketing strategy based on it. The analysis period was set from July 1, 2023, when Google Analytics 4(GA4) was integrated, to January 10, 2024, when the college admissions process was completed. The analysis revealed interesting patterns such as geographical information based on visitors' access location, devices(operating systems) and browsers used by visitors, acquisition channels through visitors traffic, conversions on pages and screens that visitors engaged with and visitor flow. Based on this study, we expect universities to find ways to strengthen their admission promotion through digital marketing and effectively communicate with applicants to gain a competitive edge.