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Derivation of accident reduction measures through analysis of bicycle accident occurrences: Centering on Gyeonggi-do Province


Byeong Kwan, Jeon, Jong Kyu, Ko, Gouzhong, Li, Vol. 5, No. 4, pp. 7-11, Dec. 2021
10.22662/IJEMR.2021.5.4.007, Full Text:
Keywords: Bicycle, Bicycle accident, Big Data Analysis, Gyeonggi-do, Accident reduction plan

Abstract

Background/Objectives: In this study, through big data analysis, the current situation and cause are identified and a plan to reduce accidents in bicycle accidents is derived. Methods/Statistical analysis: In order to secure data on bicycle traffic accidents, data from Gyeonggi-do were collected and analyzed using R and Tableau, which are big data analysis tools. After collecting the data, it was preprocessed according to the analysis. Findings: As a result of the analysis, it was found that bicycle accidents increased as the bicycle population increased. Therefore, in this study, an intersection notification and a bicycle cross-section were proposed. Improvements/Applications: The results of this study are expected to be used to develop policies that can be prevented in advance while instilling awareness of bicycle accidents.


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Cite this article
[APA Style]
Kwan, B., , Kyu, J., , , & (2021). Derivation of accident reduction measures through analysis of bicycle accident occurrences: Centering on Gyeonggi-do Province. International Journal of Emerging Multidisciplinary Research, 5(4), 7-11. DOI: 10.22662/IJEMR.2021.5.4.007.

[IEEE Style]
B. Kwan, Jeon, J. Kyu, Ko, Gouzhong, Li, "Derivation of accident reduction measures through analysis of bicycle accident occurrences: Centering on Gyeonggi-do Province," International Journal of Emerging Multidisciplinary Research, vol. 5, no. 4, pp. 7-11, 2021. DOI: 10.22662/IJEMR.2021.5.4.007.

[ACM Style]
Byeong Kwan, Jeon, Jong Kyu, Ko, Gouzhong, and Li. 2021. Derivation of accident reduction measures through analysis of bicycle accident occurrences: Centering on Gyeonggi-do Province. International Journal of Emerging Multidisciplinary Research, 5, 4, (2021), 7-11. DOI: 10.22662/IJEMR.2021.5.4.007.