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시계열모형을 활용한 춘천시 강촌역 단기수송수요 예측

Forecasting short-term transportation demand at Gangchon Station in Chuncheon-si using time series model

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초록

Purpose - This study attempted to predict short-term transportation demand using trains and getting off at Gangchon Station. Through this, we present numerical data necessary for future tourist inflow policies in the Gangchon area of Chuncheon and present related implications. Design/methodology/approach - This study collected and analyzed transportation demand data from Gangchon Station using the Gyeongchun Line and ITX-Cheongchun Train from January 2014 to August 2023. Winters exponential smoothing model and ARIMA model were used to reflect the trend and seasonality of the raw data. Findings - First, transportation demand using trains to get off at Gangchon Station in Chuncheon City is expected to show a continuous increase from 2020 until the forecast period is 2024. Second, the number of passengers getting off at Gangchon Station was found to be highest in May and October. Research implications or Originality - As transportation networks are improving nationwide and people's leisure culture is changing, the number of tourists visiting the Gangchon area in Chuncheon City is continuously decreasing. Therefore, in this study, a time series model was used to predict short-term transportation demand alighting at Gangchon Station. In order to calculate more accurate forecasts, we compared models to find an appropriate model and presented forecasts.

Abstract

Purpose - This study attempted to predict short-term transportation demand using trains and getting off at Gangchon Station. Through this, we present numerical data necessary for future tourist inflow policies in the Gangchon area of Chuncheon and present related implications. Design/methodology/approach - This study collected and analyzed transportation demand data from Gangchon Station using the Gyeongchun Line and ITX-Cheongchun Train from January 2014 to August 2023. Winters exponential smoothing model and ARIMA model were used to reflect the trend and seasonality of the raw data. Findings - First, transportation demand using trains to get off at Gangchon Station in Chuncheon City is expected to show a continuous increase from 2020 until the forecast period is 2024. Second, the number of passengers getting off at Gangchon Station was found to be highest in May and October. Research implications or Originality - As transportation networks are improving nationwide and people's leisure culture is changing, the number of tourists visiting the Gangchon area in Chuncheon City is continuously decreasing. Therefore, in this study, a time series model was used to predict short-term transportation demand alighting at Gangchon Station. In order to calculate more accurate forecasts, we compared models to find an appropriate model and presented forecasts.

참고문헌 (26)

  1. 김경범, "계절 ARIMA 모형을 이용한 제주공항 여객 수요예측 및 효율적 운영에 관한 연구", 한국산학기술학회논문지, 13(8), pp.3381~3388 (2012), 한국산학기술학회[DOI]
  2. 김관형, "개입 ARIMA 모형을 이용한 KTX 수요예측", 한국철도학회논문집, 14(5), pp.470~476 (2011), 한국철도학회
  3. 김동규, "시계열 자료를 활용한 도시철도 수요 예측", Journal of The Korean Data Analysis Society, 22(2), pp.753~765 (2020), 한국자료분석학회
  4. 김동규, "시계열분석 기반 대구 도시철도 수요 예측", 의사결정학연구, 27(1), pp.1~11 (2019)
  5. 김범승, "계절 ARIMA 모형을 이용한 여객수송수요 예측: 중앙선을 중심으로", 한국철도학회논문집, 17(4), pp.307~312 (2014), 한국철도학회
  6. 김상원, "윈터스 지수평활법을 이용한 제주도 관광객 수요예측", 한국지역경제연구, 9(2), pp.133~154 (2011), 한국지역경제학회
  7. 김상원, "제주특별자치도 관광수요 예측모델의 정확도 비교", 동북아관광연구, 14(2), pp.219~237 (2018), 동북아관광학회[DOI]
  8. 김상원, "수원시 관광객 수요예측 모델의 정확도 비교", 동북아관광연구, 12(4), pp.121~142 (2016), 동북아관광학회
  9. 김효종, "철도수요예측을 위한 직접수요모형 개발에 관한 연구", 한국철도학회 학술발표대회 논문집, pp.2166~2178 (2010)
  10. 노윤승, "SARIMA모형을 이용한 철도여객 단기수송수요 예측", 한국ITS학회 논문지, 14(4), pp.18~26 (2015), 한국ITS학회[DOI]
  11. 박득희, "방한 중국 관광객 수요 예측모델의 비교 연구", 관광연구저널, 29(3), pp.29~40 (2015), 한국관광연구학회
  12. 박득희, "지속가능한 관광성장을 위한 방한 일본인 관광객 수요예측: 시계열 계량 모형 적용", 관광연구저널, 34(3), pp.47~60 (2020), 한국관광연구학회[DOI]
  13. 방성철, "지수평활법과 ARIMA모형을 이용한 무역인력 수급예측에 관한 연구", 무역상무연구, 93, pp.177~196 (2022), 한국무역상무학회[DOI]
  14. 송근석, "관광수요 예측모형의 선정 시 표본후 예측의 중요성", 관광연구, 22(2), pp.95~116 (2007), 대한관광경영학회
  15. 송근석, "결합기법을 이용한 관광수요예측", 관광레저연구, 21(1), pp.183~202 (2009), (사)한국관광레저학회
  16. 이충기, "관광응용경제학" (2011), 일신사
  17. 최가영, "시계열분석을 통한 자연휴양림 계절별 이용수요 예측: 계절ARIMA 모형과 지수평활 모형을 중심으로", 관광경영연구, 21(3), pp.271~289 (2017), 관광경영학회
  18. Bi, J. W., "Daily tourism volume forecasting for tourist attractions", Annals of Tourism Research, 83, pp.102923~ (2020)
  19. Box, G. E., "Time series analysis: forecasting and control" (2015), John Wiley and Sons
  20. Ljung, G. M., "The likelihood function of stationary autoregressive-moving average models", Biometrika, 66(2), pp.265~270 (1979)
  21. Makridakis, S., "Forecasting Methods and Applications", pp.652~655 (1983), Wiley
  22. Palmer, A., "Designing an artificial neural network for forecasting tourism time series", Tourism management, 27(5), pp.781~790 (2006)
  23. Pan, B., "Forecasting destination weekly hotel occupancy with big data", Journal of travel research, 56(7), pp.957~970 (2017)
  24. Silva, E. S., "Forecasting tourism demand with denoised neural networks", Annals of Tourism Research, 74, pp.134~154 (2019)
  25. Vatsa, P., "Seasonality and cycles in tourism demand—redux", Annals of Tourism Research, 90, pp.103105~ (2021)
  26. Zhang, Y., "Tourism demand forecasting : A decomposed deep learning approach", Journal of Travel Research, 60(5), pp.981~997 (2021)

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