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http://dx.doi.org/10.12815/kits.2020.19.6.107

A Study on Traffic Big Data Mapping Using the Grid Index Method  

Chong, Kyu Soo (Dept. of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology)
Sung, Hong Ki (Dept. of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.19, no.6, 2020 , pp. 107-117 More about this Journal
Abstract
With the recent development of autonomous vehicles, various sensors installed in vehicles have become common, and big data generated from those sensors is increasingly being used in the transportation field. In this study, we proposed a grid index method to efficiently process real-time vehicle sensing big data and public data such as road weather. The applicability and effect of the proposed grid space division method and grid ID generation method were analyzed. We created virtual data based on DTG data and mapped to the road link based on coordinates. As a result of analyzing the data processing speed in grid index method, the data processing performance improved by more than 2,400 times compared to the existing link unit processing method. In addition, in order to analyze the efficiency of the proposed technology, the virtually generated data was mapped and visualized.
Keywords
Vehicle sensing data; Grid index; Traffic big data; Mapping;
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