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[B2024-10] Characteristics Analysis of Commuting and Schooling in Chun… New postHot issue
Writer : 서브관리자 Views : 28

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 [ABSTRACT]


This study aims to diagnose the commuting and school commuting (hereafter, commuting) characteristics of Chungcheongbuk-do, previously unidentified, by collecting and processing mobile big data related to commuting in the province. Using data from October 17 to October 21, 2022 (Monday to Friday), this study analyzes commuting times and their characteristics at the eup-myeon-dong level. The analysis reveals that the average commuting time in Chungcheongbuk-do is approximately 49.1 minutes. Specifically, the average commuting time for work is 52.7 minutes (24.6 minutes for the outbound trip and 28.1 minutes for the return trip), while the average commuting time for school is 28.0 minutes (13.8 minutes for the outbound trip and 14.2 minutes for the return trip). Eumseong-gun and Jincheon-gun have the longest average commuting times at 57.2 minutes and 56.3 minutes, respectively, whereas Cheongju-si, Chungju-si, and Jecheon-si, with higher population densities, record shorter times of 48.9 minutes, 44.6 minutes, and 40.7 minutes, respectively. At the eup-myeon-dong level, urban areas show smaller variations in commuting times, while rural areas exhibit greater disparities. When analyzed by age group, individuals in their 50s have the longest commuting times at 55.6 minutes. Male residents spend, on average, 8.5 minutes more on commuting than females and represent a higher commuting population. Regarding commuting patterns, 27.2% of movements occur within the same eup-myeon-dong, 57.1% involve movements between different eup-myeon-dong within the same city or county, and 15.7% are inter-regional movements to other municipalities. This study highlights the utility of mobile big data in uncovering nuanced commuting characteristics in Chungcheongbuk-do. The findings provide foundational insights that can inform regional policies and strategies to enhance mobility and promote sustainable development. While many questions remain, this research emphasizes the need for further studies leveraging big data to support regional growth and innovation.

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