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2024 Vol.42, Issue 3

Article

Analysis of Mobility Changes by Implementing Social Distancing Policies

사회적 거리두기 정책 시행에 따른 모빌리티 변동 분석

Bumchul CHO1
,
Deokbae AN2*
조 범철1
,
안 덕배2*
1Chief Director, Department of Transport Big Data, Korea Transport Institute, Sejong 30147, Korea
2Research Specialist, Department of Transport Big Data, Korea Transport Institute, Sejong 30147, Korea
1한국교통연구원 교통빅데이터연구본부 본부장
2한국교통연구원 교통빅데이터연구본부 전문연구원
*Corresponding Author
30 June 2024. pp. 245-267
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Abstract

The impact of the COVID-19 Pandemic and the policies introduced to respond to it has drastically changed people's mobility patterns. As a result, various changes emerged from the perspective of mobility that could not be observed due to past socioeconomic changes or policy implementation. In previous studies, it was difficult to find studies that explained changes in mobilioty according to mobility management policies during Pandemic. Therefore, this study explores the factors that influenced the mobility changes observed during the COVID-19 Pandemic to identify causal relationships and analyze the impact of mobility management policies. Using mobile data, we analyze changes in household-based irregular trips and night trips in late 2020, and find that changes in the number of confirmed cases have a short-term impact on mobility changes, while changes in social distancing policies have a medium- to long-term impact on mobility changes. In particular, mobility changes due to increased social distancing levels were observed to be larger and more prolonged for night trips.

Keywords
mobile data
mobility change
policy impact analysis
social distancing
vector auto regressive

코로나19 팬데믹과 이에 대응하기 위해 도입된 정책에 의한 충격은 국민의 통행패턴을 급격하게 변화시켰다. 그 결과 통행의 관점에서 과거 사회경제 변화나 정책 시행으로 관측할 수 없었던 다양한 변화가 나타났다. 선행 연구에서는 실제 이동성 관리 정책에 따른 통행의 변화를 설명한 연구는 찾아보기 어려웠다. 따라서 본 연구에서는 코로나19 팬데믹 기간 중 관측된 모빌리티 변화에 영향을 준 요인을 탐색하여 인과관계를 규명하고, 이를 통해 이동성 관리 정책이 모빌리티 변화에 미친 충격을 분석하고자 하였다. 모바일 기지국 데이터를 활용하여 2020년 말의 가정기반 비정기통행과 심야 귀가통행 변화에 대하여 분석하였으며, 분석 결과 확진자 수의 변화는 모빌리티 변동에 단기적인 영향을 미치는 데 반해, 사회적 거리두기 정책의 변화는 모빌리티 변동에 중장기적인 영향을 미치는 것으로 나타났다. 특히, 대중교통 야간 운행 감축 및 심야영업 제한 등 거리두기 단계 상향에 따른 모빌리티 변동은 심야 통행에서 더욱 크고 장기적으로 관찰되었다.

키워드
모빌리티 빅데이터
모빌리티 변동
정책영향분석
사회적 거리두기
벡터자기회귀
References
1

Asian Development Bank (2021), Assessment of COVID-19 Response in the Republic of Korea, Asian Development Bank, Mandaluyong, Philippines.

2

Cho B., Kwon K., An D. (2021), Impact Analysis of Mobility for Policy Change, Korea Transport Institute, Sejong, South Korea.

3

Cho J. (2014), A Study on Demand Forecasting of Export Goods Based on Vector Autoregressive Model: Subject to Each Small Passenger Vehicles Quarterly Exported to USA, International Commerce and Information Review, 16(3), 73-96.

10.15798/kaici.16.3.201406.73
4

Chun H. (2014), An Empirical Analysis on the Relationship between Economic Growth and Income Inequality, Seoul Studies, 15(2), 95-111.

5

Fekih M., Bellemans T., Smoreda Z., Bonnel P., Furno A., Galland S. (2019), Sustainability of Cellular Network Signaling Data for Origin-Destination Matrix Construction: A Case Study of Lyon Region (France), Proceedings of 98th Annual Meeting Transportation Research Board, Washington D.C.

6

Fisher M., LaMondia J. J. (2023), Understanding the Temporal, Regional, Demographic, and Policy Factors Influencing Counties' Daily Traffic Volume Reductions in Response to COVID-19, Transportation Research Record, 2677(4), 129-141.

10.1177/0361198121100954137153187PMC10149349
7

Hamid S., Zandiatashbar A. (2021), Compact Development and Adherence to Stay-at-home Order during the COVID-19 Pandemic: A Longitudinal Investigation in the United States, Landscape and Urban Planning, 205, 103952.

10.1016/j.landurbplan.2020.10395233020675PMC7526615
8

Han K., Kim D., Kang W., So J. (Jason), Lee C. (2021), Analysis of the Current Status and Correlation of Traffic Demand according to the COVID-19 Indicator, Journal of The Korea Institute of Intelligent Transport Systems, 20(6), 55-65.

10.12815/kits.2021.20.6.55
9

https://korea.kr/news

10

Jang D., Won M., Cho C., Choi J. (2021), A Study on the Response Strategy for POST COVID-19 by Transportation Sector, Korea Transport Institute, Sejong, South Korea.

11

Jung Y., Kim Y. (2020), 2020 Broadcasting Media Usage Behavior Survey, Korea Communications Commission, Gwacheon, South Korea.

12

Kim C., Nam J. (2015), A Causality Analysis of the Hairtail Price by Distribution Channel using a VectorAutoregressive Model, Journal of Fisheries Business Administration, 46(1), 93-107.

10.12939/FBA.2015.46.1.093
13

Kim M., Kim H. (2023), The Effects of COVID-19 on Public Transportation Demand: The Case of Busan Metropolitan City, Journal of the Korean Association of Geographic Information Studies, 26(3), 1-11.

14

Kim S., Jung H. (2022), Influence of COVID-19 on Public Transportation Mode Changes and Countermeasures, Journal of Civil and Environmental Engineering Research, 42(3), 379-389.

15

Lee J. (2021), A Study on the Impacts on transportation Behavior by Corona 19 in Daejeon City, Daejeon Sejong Research Institute, Daejeon, South Korea.

16

Lee S., Chae S., Lee C., Ahn S., Jang K. (2020), Effects of COVID-19 on Trip Patterns in Daejeon Metropolitan City, South Korea, Transportation Technology and Policy, 17(4), 17-27.

17

Lee S., Shin S., Nam D., Kim J., Park J. (2021), The Analysis Correlation Subway and Bike Sharing Ridership before and during COVID-19 Pandemic in Seoul, Journal of The Korea Institute of Intelligent Transport Systems, 20(6), 14-25.

10.12815/kits.2021.20.6.14
18

Lim S. (2020), An Analysis of Change in Traffic Demand with Coronavirus Disease 2019, Journal of Korea Institute of Intelligent Transport Systems, 19(5), 106-118.

10.12815/kits.2020.19.5.106
19

Moon K. (1997), A Understanding of Vector Autoregressive Model, Journal of The Korean Official Statistics, 2(1), 23-56.

20

Shin S., Choo S. (2022), Exploring the Influencing Factors on Change in Use of Sharing Mobility by the COVID-19 Pandemic: Focused on Residents in New Town, J. Korean Soc. Transp., 40(2), Korean Society of Transportation, 230-244.

10.7470/jkst.2022.40.2.230
21

Shirgaokar M., Misra A., Nobler E., Muresan D. (2021), What can U.S. Travel Data tell Planners about Opening the Economy during COVID-19?, Proceedings of 101st Annual Meeting Transportation Research Board, Washington D.C.

22

Yang S., Park J. (2023), An Analysis of the Effect of Commercial Environment in the Neighborhood on Changes in Leisure Trip after COVID-19 Outbreak, Journal of Korea Planning Association, 58(4), 30-47.

10.17208/jkpa.2023.08.58.4.30
23

Zhexue H. (1998), Extension to the K-means Algorithm for Clustering Large Data Sets with Categorical Values, Data Mining and Knowledge Discovery, 2, 283-304.

10.1023/A:1009769707641
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 42
  • No :3
  • Pages :245-267
  • Received Date : 2023-10-05
  • Revised Date : 2023-11-06
  • Accepted Date : 2024-05-08
  • DOI :https://doi.org/10.7470/jkst.2024.42.3.245
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