• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.19-31
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• 2001

In this paper, 3 layered architecture model and related design guidelines are proposed, which have been actually applied in our national ITS-Architecture design. The domain architecture as the 1st layer is to structure all ITS related domains for maximizing the co-operability in national level. The logical architecture as the 2nd layer is to structure all ITS related application-systems for minimizing duplications, conflicts and dead-zones in service level and maximizing the co-operability in application-system level. The physical architecture as the 3rd layer is to structure all IT(Information Technology) related physical resources for maximizing.

• 한국ITS학회:학술대회논문집
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• 2003.11a
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• pp.73-75
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• 2003

• 한국ITS학회:학술대회논문집
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• 2010.05a
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• pp.1-4
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• 2010

• 한국ITS학회:학술대회논문집
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• v.2007 no.10
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• pp.266-269
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• 2007

• 한국ITS학회:학술대회논문집
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• 2002.11a
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• pp.125-128
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• 2002

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.5
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• pp.61-72
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• 2013

Next-generation ITS(Intelligent Transportation System) adopts WAVE(Wireless Access in Vehicular Environment) system which is capable of the bidirectional communication system in vehicular environments. u-Transportation system adopted WAVE communications system to show the optimal performance in terms of various services with regard to vehicle safety and traffic. In this paper, we introduce testbed of ubiquitous-Transportation system and its service. Then, we describe WAVE system for supporting next-generation ITS service. Also, we carried out tests in real road environments in order to verify communication functions of WAVE systems and its performance. We confirmed that our communication systems for supporting services meet the communication performance.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.163-171
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• 2023

The intelligent transportation system has made a huge leap in the level of human services, which has had a positive impact on the quality of life of users. On the other hand, these services are becoming a new source of risk due to the use of data collected from vehicles, on which intelligent systems rely to create automatic contextual adaptation. Most of the popular privacy protection methods, such as Dummy and obfuscation, cannot be used with many services because of their impact on the accuracy of the service provided itself, they depend on changing the number of vehicles or their physical locations. This research presents a new approach based on the shuffling Nicknames of vehicles. It fully maintains the quality of the service and prevents tracking users permanently, penetrating their privacy, revealing their whereabouts, or discovering additional details about the nature of their behavior and movements. Our approach is based on creating a central Nicknames Pool in the cloud as well as distributed subpools in fog nodes to avoid intelligent delays and overloading of the central architecture. Finally, we will prove by simulation and discussion by examples the superiority of the proposed approach and its ability to adapt to new services and provide an effective level of protection. In the comparison, we will rely on the wellknown privacy criteria: Entropy, Ubiquity, and Performance.

• Journal of Korean Society of Transportation
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• v.20 no.6
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• pp.151-152
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• 2002

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.2
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• pp.14-23
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• 2015

This study was intended to evaluate the feasibility of the vehicle detector using a coil sensor. For the evaluation, the research team built a test environment for the detector consisting of a oscillation circuit, data collecting circuit, data monitoring and saving circuit, etc. As the result of the frequency analysis of the detector from the test environment, it was verified for the detector using a coil sensor to generate stable frequencies. In addition, the ease of construction and management was tested by comparing the size of cutting areas, consumption of installation materials, and installation time for a traditional loop detector and the detector using a coil sensor. As a result, the installation of the detector using a coil sensor requires less size of cutting areas, consumption of installation materials, and installation time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.1-16
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• 2023

Accurate traffic information prediction is considered to be one of the most important aspects of intelligent transport systems(ITS), as it can be used to guide users of transportation facilities to avoid congested routes. Various deep learning models have been developed for accurate traffic prediction. Recently, ensemble techniques have been utilized to combine the strengths and weaknesses of various models in various ways to improve prediction accuracy and stability. Therefore, in this study, we developed and evaluated a traffic information prediction model using various deep learning models, and evaluated the performance of the developed deep learning models as a stacking ensemble. The individual models showed error rates within 10% for traffic volume prediction and 3% for speed prediction. The ensemble model showed higher accuracy compared to other models when no cross-validation was performed, and when cross-validation was performed, it showed a uniform error rate in long-term forecasting.