Causal inference of Seoul bike sharing demand
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Abstract
Abstract
The global surge in environmental consciousness has significantly boosted the demand for rental bikes, particularly in metropolitan areas such as Seoul. This study delves into the causal relationships affecting this demand using a dataset from Seoul's bike-sharing system. Unlike previous research focusing predominantly on predictive analytics, this work innovatively applies multiple linear regression models to uncover causal inferences, offering insights that extend beyond mere forecasting. The challenges addressed include dealing with non-linear relationships and heteroscedasticity by employing the logarithmic transformation of rental counts. This approach not only aids in normalizing the data but also enhances the interpretability of the regression outcomes, emphasizing the changes in demand as a function of various environmental and temporal variables. Recent developments in causal inference methodologies have allowed for more robust and detailed analysis, paving the way for this study's contribution to the field. The findings underscore the significant influence of factors such as hour of the day, humidity, and seasonal changes on bike rental volumes, which can inform policy-making and operational strategies in urban transport planning.
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Faghih-Imani A, Eluru N, El-Geneidy AM, Rabbat M, Haq U. How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal. Journal of Transport Geography. 2014; 41: 306–14. doi: 10.1016/j.jtrangeo.2014.01.013
Fishman E, Washington S, Haworth N. Bike share: a synthesis of the literature. Transport Reviews. 2013;33(2):148–65. doi: 10.1080/01441647.2013.775612
Gebhart K, Noland RB. The impact of weather conditions on bikeshare trips in Washington, DC. Transportation. 2014;41(6):1205–25. doi: 10.1007/s11116-014-9540-7
Zhang Y, Thomas T, Brussel M, van Maarseveen M. Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan, China. Journal of Transport Geography. 2017; 58: 59–70. doi: 10.1016/j.jtrangeo.2016.11.014
Frade I, Ribeiro A. Bicycle sharing systems demand. Procedia - Social and Behavioral Sciences. 2014;111:518–27. doi: 10.1016/j.sbspro.2014.01.085
Borgnat P, Abry P, Flandrin P, Robardet C, Rouquier JB, Fleury E. Shared bicycles in a city: A signal processing and data analysis perspective. Advances in Complex Systems. 2011;14(03):415–38. doi: 10.1142/S0219525911002950
Lin L, He Z, Peeta S. Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach. Transportation Research Part C: Emerging Technologies. 2018; 97: 258–76. doi: 10.1016/j.trc.2018.10.011
Xu C, Ji J, Liu P. The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets. Transportation Research Part C: Emerging Technologies. 2018; 95, 47-60. doi: 10.1016/j.trc.2018.07.013
Pearl J. Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press. 2009. doi: 10.1017/CBO9780511803161
Imbens GW, Rubin DB. Causal inference in statistics, social, and biomedical sciences: An introduction. Cambridge University Press. 2015. doi: 10.1017/CBO9781139025751
Bao J, He T, Ruan S, Li Y, Zheng Y. Planning bike lanes based on sharing-bikes' trajectories. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017;1377–86. doi: 10.1145/3097983.3098056
An R, Zahnow R, Pojani D, Corcoran J. Weather and cycling in New York: The case of Citibike. Journal of Transport Geography. 2019; 77: 97–112. doi: 10.1145/3097983.3098056
Caulfield B, O'Mahony M, Brazil W, Weldon P. Examining usage patterns of a bike-sharing scheme in a medium sized city. Transportation Research Part A: Policy and Practice. 2017; 100: 152–61. doi: 10.1016/j.tra.2017.04.023
de Chardon CM, Caruso G, Thomas I. Bicycle sharing system 'success' determinants. Transportation Research Part A: Policy and Practice. 2017; 100: 202–14. doi: 10.1016/j.tra.2017.04.020
El-Assi W, Salah Mahmoud M, Nurul Habib K. Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto. Transportation. 2017; 44(3): 589–613. doi: 10.1007/s11116-015-9669-z
Wang K, Akar G, Chen Y-J. Bike sharing differences among Millennials, Gen Xers, and Baby Boomers: Lessons learnt from New York City's bike share. Transportation Research Part A: Policy and Practice. 2018; 116: 1–14. doi: 10.1007/s11116-015-9669-z
Noland RB, Smart MJ, Guo Z. Bikeshare trip generation in New York City. Transportation Research Part A: Policy and Practice. 2016; 94: 164–81. doi: 10.1016/j.tra.2016.08.030
Faghih-Imani A, Eluru N. Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system. Journal of Transport Geography. 2016; 54: 218–27. doi: 10.1016/j.jtrangeo.2016.06.008
Hyland M, Frei C, Frei A, Mahmassani HS. Riders on the storm: Exploring weather and seasonality effects on commute mode choice in Chicago. Travel Behaviour and Society. 2018; 13: 44–60. doi: 10.1016/j.tbs.2018.05.001
Guo Y, Zhou J, Wu Y, Li Z. Identifying the factors affecting bike-sharing usage and degree of satisfaction in Ningbo, China. PLoS One. 2017 Sep 21;12(9):e0185100. doi: 10.1371/journal.pone.0185100. PMID: 28934321; PMCID: PMC5608320.
Ashqar HI, Elhenawy M, Rakha HA. Modeling bike counts in a bike-sharing system considering the effect of weather conditions. Case Studies on Transport Policy. 2019; 7(2): 261–268. doi: 10.1016/j.cstp.2019.02.011
Shen Y, Zhang X, Zhao J. Understanding the usage of dockless bike sharing in Singapore. International Journal of Sustainable Transportation. 2018;12(9):686–700. doi: 10.1080/15568318.2018.1429696
Sun F, Chen P, Jiao J. Promoting public bike-sharing: A lesson from the unsuccessful Pronto system. Transp Res D Transp Environ. 2018 Aug;63:533-547. doi: 10.1016/j.trd.2018.06.021. Epub 2018 Jun 28. PMID: 37928131; PMCID: PMC10624502.
Böcker L, Anderson E, Uteng TP, Throndsen T. Bike sharing use in conjunction to public transport: Exploring spatiotemporal, age and gender dimensions in Oslo, Norway. Transportation Research Part A: Policy and Practice. 2020; 138: 389–401. doi: 10.1016/j.tra.2020.06.019
Li R, Sinniah KG, Li X. The Factors Influencing Resident’s Intentions on E-Bike Sharing Usage in China. Sustainability. 2022, 14, 5013. doi: 10.3390/su14095013
Morton C. Appraising the market for bicycle sharing schemes: Perceived service quality, satisfaction, and behavioural intention in London. Case Studies on Transport Policy. 2018; 6(1): 102–111. doi: 10.1016/j.cstp.2017.11.003
de Chardon MC. The contradictions of bike-share benefits, purposes and outcomes. Transportation Research Part A: Policy and Practice. 2019; 121: 401–19. doi: 10.1016/j.tra.2019.01.031
Eren E, Uz VE. A review on bike-sharing: The factors affecting bike-sharing demand. Sustainable Cities and Society. 2020; 54: 101882. doi: 10.1016/j.scs.2019.101882
Reck DJ, Axhausen KW. Who uses shared micro-mobility services? Empirical evidence from Zurich, Switzerland. Transportation Research Part D: Transport and Environment. 2021; 94: 102803. doi: 10.1016/j.trd.2021.102803
Gu T, Kim I, Currie G. To be or not to be dockless: Empirical analysis of dockless bikeshare development in China. Transportation Research Part A: Policy and Practice. 2019; 119: 122–47. doi: 10.1016/j.tra.2018.11.007
UCI Machine Learning Repository. Seoul Bike Sharing Demand Data Set. UCI Machine Learning Repository” 2020. http://archive.ics.uci.edu/ml/datasets/Seoul+Bike+Sharing+Demand doi: 10.24432/C5F62R
Wooldridge JM. Introductory Econometrics: A Modern Approach. 5th ed. Mason, OH: South-Western Cengage Learning. 2013. https://archive.org/details/introductoryecon0000wool_c3l8.