Analysis of Public Transport Ridership During a Heavy Snowfall in Seoul
- 19 Downloads
Severe weather conditions, such as heavy snowfall, rain, heatwave, etc., may affect travel behaviors of people and finally change traffic patterns in transportation networks. 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. 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.
KeywordsWeather condition Public transport ridership Card data Mobile phone data Decision-Tree model
This study was funded by the Korea Meteorological Administration, Korea Meteorological Institute (Grant No. KMI2018-04910).
- 4.Kashfi, S., et al.: Impact of rain on daily bus ridership: a Brisbane case study. In: Australasian Transport Research Forum 2013 Proceedings. Australian Transportation Research Forum (2013)Google Scholar
- 7.Chung, W.-Y., et al.: A study on local area weather condition monitoring system in WSN and CDMA. J. Korea Inst. Marit. Inf. Commun. Sci. 13(8), 1712–1720 (2009)Google Scholar
- 8.Aggarwal, C.C.: Outlier Analysis (2013)Google Scholar
- 9.Han, J., et al.: Data mining concepts and techniques third edition. In: The Morgan Kaufmann Series in Data Management Systems, pp. 83–124 (2011)Google Scholar
- 10.Breiman, L.: Classification and Regression Trees. Routledge (2017)Google Scholar
- 11.Sung, H., Kim, T.-H.: A study on categorizing subway station areas in Seoul by rail use pattern. J. Korean Soc. Transp. 49, 455–464 (2005)Google Scholar
- 12.Kim, J.: 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. J. Korean Geogr. Soc. 44(6), 797–812 (2009)Google Scholar
- 14.Seoul Data Center. http://data.seoul.go.kr
- 15.The Korea Transport Institute (2015). Unpublished report (in Korean)Google Scholar