• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.162-169
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• 2022

In a dynamically changing time-varying network environment, the optimal moving pattern of edge devices can be applied to distributing computing resources to edge cloud servers or deploying new edge servers in the FEC(Fog/Edge Computing) environment. In addition, this can be used to build an environment capable of efficient computation offloading to alleviate latency problems, which are disadvantages of cloud computing. This paper proposes an algorithm to extract the optimal moving pattern by analyzing the moving path of multiple edge devices requiring application services in an arbitrary spatio-temporal environment based on frequency. A comparative experiment with A^* and Dijkstra algorithms shows that the proposed algorithm uses a relatively fast execution time and less memory, and extracts a more accurate optimal path. Furthermore, it was deduced from the comparison result with the A^* algorithm that applying weights (preference, congestion, etc.) simultaneously with frequency can increase path extraction accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.786-792
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• 2022

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A^* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments.

• Journal of the Korean earth science society
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• v.33 no.6
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• pp.469-480
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• 2012

This study was to analyze the characteristics of diurnal variation of high $PM_{2.5}$ concentration, $PM_{2.5}/PM_{10}$ concentration ratio by spatio-temporal wind system (wind speed and wind direction) for high $PM_{2.5}$ concentration (over the 24 hr environmental standard of $PM_{2.5}$, $50{\mu}g/m^3$) in the air quality observation sites (Jangrimdong: Industrial area, Jwadong: Residential area) that were measured for 3 years (2005. 12. 1-2008. 11. 30) in Busan. The observation days of high $PM_{2.5}$ concentration were 182 at Jangrimdong and 27 at Jwadong. The seasonal diurnal variation of hourly mean of high $PM_{2.5}$ concentration and of $PM_{2.5}/PM_{10}$ concentration ratio showed a similar pattern that had higher variation at dawn, and night and in the morning than in the afternoon. Durning daytime in summer at Jwadong, the $PM_{2.5}/PM_{10}$ concentration ratio increased because a secondary particulate matter, which was created by photochemical reaction, decreased the coarse particles of $PM_{10}$ more than the fine particles of $PM_{2.5}$ concentrations in ocean condition. We did an analysis of spatio-temporal wind system (wind speed range and wind direction) in each time zone. The result showed that high $PM_{2.5}$ concentration at Jangrimdong occurred due to the congestion of pollutants emissions from the industrial complex in Jangrimdong area and the transportation of pollutants from places nearby Jangrimdong. It also showed that high $PM_{2.5}$ concentration occurred at Jwadong because of a number of local residential and commercial activities that caused the congestion of pollutants.