Journal of the Korean Society for Railway (한국철도학회논문집)

The Korean Society for Railway (한국철도학회)
권호 표지
DOI QR코드

DOI QR Code

KTX Passenger Demand Forecast with Intervention ARIMA Model

개입 ARIMA 모형을 이용한 KTX 수요예측

  • Received : 2011.10.05
  • Accepted : 2011.10.26
  • Published : 2011.10.26
Download PDF
⟨ Previous Next ⟩

Abstract

This study proposed the intervention ARIMA model as a way to forecast the KTX passenger demand. The second phase of the Gyeongbu high-speed rail project and the financial crisis in 2008 were analyzed in order to determine the effect of time series on the opening of a new line and economic impact. As a result, the financial crisis showed that there is no statistically significant impact, but the second phase of the Gyeongbu high-speed rail project showed that the weekday trips increased about 17,000 trips/day and the weekend trips increased about 26,000 trips/day. This study is meaningful in that the intervention explained the phenomena affecting the time series of KTX trip and analyzed the impact on intervention of time series quantitatively. The developed model can be used to forecast the outline of the overall KTX demand and to validate the KTX O/D forecasting demand.

본 연구는 KTX 수요를 예측하기 위한 방법으로 개입 ARIMA 모형을 제안하였다. 신선개통과 경제충격으로 인한 시계열의 영향 여부를 파악하기 위해 경부고속철도 2단계 개통과 2008년 금융위기를 분석하였다. 분석결과 금융위기는 통계적으로 유의미한 영향이 없는 것으로 나타났으나, 경부고속철도 2단계는 주중 통행량 17,000 통행/일, 주말 통행량 26,000 통행/일 정도 증가한 것으로 나타났다. 본 연구는 개입이 통행량 시계열에 영향을 미치는 현상을 파악하고, 시계열 자료에 대한 개입효과를 계량적으로 분석했다는 점에서 의의가 있다. 개발된 모형은 KTX 전체 수요를 개략적으로 예측하는데 활용될 수 있으며, KTX O/D별 예측치를 검증하는데 활용이 가능하다.

Keywords

References

  1. D. Lee, T. Hong, H. Kim, K. Woo (1998) Study on the Demand Prediction for Transportation System Utilizing Data Granulization, Proceedings of the Conference of the Korean Society for Railway, pp. 211-218.
  2. S. Oh, S. Hong (2000) A study on the effect factors of the railway passenger demand forecasting by the disaggregate model, Proceedings of the Conference of the Korean Institute of Electrical Engineers, pp. 1445-1447.
  3. S. Oh, D. Kim (2001) A Study on the Seasonal Decomposition of the Railway Passenger Demand, Proceedings of the Conference of the Korean Society for Railway, pp. 111-116.
  4. T. Choi, S. Kim (2004) An Empirical Comparison among Initialization Methods of Holt-Winters Model for Railway Passenger Demand Forecast, Journal of the Korean Society for Railway, 7(1), pp. 9-13.
  5. T. Tsai, C. Lee, C. Wei (2005) Design of dynamic neural networks to forecast short-term railway passenger demand, Journal of the Eastern Asia Society for Transportation Studies, 6, pp. 1651-1666.
  6. H. Kim, D. Yun (2011) Travel Behavior Analysis for Shortterm Railroad Passenger Demand Forecasting in KTX, Proceedings of the Conference of the Korean Society for Railway, pp. 1282-1289.
  7. G. Box, G. Tiao (1975) Intervention analysis with applications to economic and environmental problems, Journal of the American Statistical Association, 70(349), pp. 70-79. https://doi.org/10.1080/01621459.1975.10480264
  8. S. Cho, Y. Son (2010) Time Series Analysis using SAS/ETS, Yulgok Books, Seoul.
  9. S. Mo (2011) Demand Pattern of the Global Passengers: Sea and Air Transport, Journal of Korea Port Economic Association, 27(1), pp. 1-11.

Cited by

  1. Forecasting Passenger Transport Demand Using Seasonal ARIMA Model - Focused on Joongang Line vol.17, pp.4, 2014, https://doi.org/10.7782/JKSR.2014.17.4.307
  2. Short-term Railway Passenger Demand Forecasting by SARIMA Model vol.14, pp.4, 2015, https://doi.org/10.12815/kits.2015.14.4.018
  3. Relationship Identification of Diffusion Effect on High-speed Rail Demand Increase vol.19, pp.4, 2016, https://doi.org/10.7782/JKSR.2016.19.4.539