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Analysis of Public Transport Ridership during a Heavy Snowfall in Seoul

기상상황에 따른 서울시 대중교통 이용 변화 분석: 폭설을 중심으로

  • Received : 2019.11.07
  • Accepted : 2019.11.17
  • Published : 2019.12.01

Abstract

Severe weather conditions, such as heavy snowfall, rain, heatwave, etc., may affect travel behaviors of people and finally change traffic patterns in transportation networks. To deal with those changes and prevent any negative impacts on the transportation system, understanding those impacts of severe weather conditions on the travel patterns is one of the critical issues in the transportation fields. Hence, this study has focused on the impacts of a weather condition on travel patterns of public transportations, especially when a heavy snowfall which is one of the most critical weather conditions. First, this study has figured out the most significant weather condition affecting changes of public transport ridership using weather information, card data for public transportation, mobile phone data; and then, developed a decision-tree model to determine complex inter-relations between various factors such as socio-economic indicators, transportation-related information, etc. As a result, the trip generation of public transportations in Seoul during a heavy snowfall is mostly related to average access times to subway stations by walk and the number of available parking lots and spaces. Meanwhile, the trip attraction is more related to business and employment densities in that destination.

기상상황(폭염, 폭우, 한파, 폭설)은 대중교통 통행 및 이용 패턴에 영향을 미치는 중요한 변수 중의 하나이며, 시스템의 예측가능성과 안정성을 중시하는 교통분야에서 이러한 기상의 영향을 이해하는 것은 매우 중요한 요소중의 하나이다. 그러므로 본 연구에서는, 서울시를 대상으로 기상상황에 따른 대중교통 이용 변화를 분석하고 해석하고자 하였다. 먼저, 기상, 모바일폰통신, 대중교통카드 자료를 이용하여 각 기상 상황별 서울시 대중교통 이용 변화를 살펴보고, 가장 영향이 큰 폭설 상황을 기준으로 대중교통 이용패턴을 지역별로 분석하였다. 또한, 의사결정모델(Decision-tree Model)를 활용하여 각 영향 변수들 간의 복잡한 관계를 밝히고자 하였다. 분석결과, 폭설 시, 전체 통행에 대한 잠재수요는 감소하고, 대중교통으로의 수단 전환이 일어나는 것을 확인할 수 있었다. 또한, 강동 및 송파 지역과 강서, 구로, 양천, 영등포 지역은 대중교통 이용이 증가하였으며, 관악, 금천, 동작 지역은 상대적으로 큰 변화가 없는 것을 확인할 수 있었다. 마지막으로, 폭설 시 대중교통 출발량의 변화는 도보접근통행시간, 정류장 근처 주차 가용성 등이 중요한 역할을 하며, 도착량의 변화는 해당 도착지의 종사자 및 사업체 밀도와 밀접한 연관이 있다는 것을 알 수 있었다.

Keywords

References

  1. Aggarwal, C. C. (2013). Outlier analysis, Springer, Berlin.
  2. Arana, P., Cabezudo, S. and Penalba, M. (2014). "Influence of weather conditions on transit ridership: A statistical study using data from smartcards." Transportation research part A: policy and practice, Vol. 59, pp. 1-12. https://doi.org/10.1016/j.tra.2013.10.019
  3. Breiman, L. (2017). Classification and regression trees, Routledge.
  4. Choi, S. G., Rhee, J. H. and Oh, S. H. (2013). "The effect of weather conditions on transit ridership." J. Korean Soc. Civ. Eng., KSCE, Vol. 33, No. 6, pp. 2447-2453 (in Korean). https://doi.org/10.12652/Ksce.2013.33.6.2447
  5. Chung, W. Y., Jung, S. J., Kim, J. J. and Kwon, T. H. (2009). "A study on local area weather condition monitoring system in WSN and CDMA." The journal of the Korea Institute of Maritime Information & Communication Sciences, Vol. 13, No. 8, pp. 1713-1720 (in Korean).
  6. Han, J., Kamber, M. and Pei, J. (2011). Data mining: concepts and techniques third edition, The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers, Massachusetts, pp. 83-124.
  7. Kashfi, S. A., Lee, J. and Bunker, J. (2013). "Impact of rain on daily bus ridership: a Brisbane case study." Australasian Transport Research Forum 2013 Proceedings 2-4 October 2013, Brisbane, Australia.
  8. Kim, H. J., Oh, S. and Kim, U. M. (2017). "A study on the prediction of public transportation consumption in seoul by weather." Proceedings of the Korea Information Processing Society Conference, Korea Information Processing Society, pp. 656-659 (in Korean).
  9. Kim, J. (2009). "An analysis of the changes in the cause-and-effect relationships between socio-economic indicators and the road network of seoul using structural equation model." Journal of the Korean Geographical Society, Vol. 44, No. 6, pp. 797-812 (in Korean).
  10. Lee, J. H. and Jung, H. Y. (2018). "The impact of weather conditions on transit ridership using quantile regression analysis." Journal of Korea Planning Association, Vol. 53, No. 4, pp. 95-106 (in Korean). https://doi.org/10.17208/jkpa.2018.08.53.4.95
  11. Lee, J., Go, J. Y., Jeon, S. and Jun, C., (2015). "A study of land use characteristics by types of subway station areas in Seoul analyzing patterns of transit ridership." The Korea Spatial Planning Review, Vol. 84, pp. 35-53 (in Korean). https://doi.org/10.15793/kspr.2015.84..003
  12. Lee, K. S., Eom, J. K., Min, J. H. and Yang, K. Y. (2014). "The Impact of rain on public transit ridership in Seoul." Journal of the Korean Society of Railway, Vol. 2014, No. 5, pp. 252-257 (in Korean).
  13. Park, K. and Lee, S. (2012). "A study on the effect of adverse weather conditions on public transportation mode choice." J. Korean Soc. Civ. Eng., KSCE, Vol. 32, No. 1, pp. 23-31 (in Korean).
  14. Seoul Metropolitan Government (2018). Seoul data center, Available at: http://data.seoul.go.kr (Accessed: November 4, 2019) (in Korean).
  15. Shin, K. and Choe, G. J. (2014). "Analyzing the relationship between precipitation and transit ridership through a seemingly unrelated regression model." Journal of Korean Society of Transportation, Vol. 32, No. 2, pp. 83-92 (in Korean). https://doi.org/10.7470/jkst.2014.32.2.083
  16. Stover, V. W. and McCormack, E. D. (2012). "The impact of weather on bus ridership in Pierce County, Washington." Journal of Public Transportation, Vol. 15, No. 1, pp. 95-110. https://doi.org/10.5038/2375-0901.15.1.6
  17. Sung, H. and Kim, T. H. (2005). "A study on categorizing subway station areas in Seoul by rail use pattern." Journal of Korean Society of Transportation, Vol. 23, No. 8, pp. 19-29 (in Korean).
  18. The Korea Transport Institute (2015). Unpublished report (in Korean).
  19. Won, M., Kim, H. and Chang, G. L. (2018). "Knowledge-based system for estimating incident clearance duration for maryland I-95." Transportation Research Record: Journal of the Transportation Research Board, Vol. 2672, No. 14, pp. 61-72. https://doi.org/10.1177/0361198118792119
  20. Zhou, M., Wang, D., Li, Q., Yue, Y., Tu, W. and Cao, R. (2017). "Impacts of weather on public transport ridership: Results from mining data from different sources." Transportation Research Part C: Emerging Technologies, Vol. 75, pp. 17-29. https://doi.org/10.1016/j.trc.2016.12.001