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Articles

Factors affecting bike-sharing system demand by inferred trip purpose: Integration of clustering of travel patterns and geospatial data analysis

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Pages 847-860 | Received 03 Feb 2021, Accepted 09 Jun 2021, Published online: 12 Jul 2021

References

  • Akar, G., & Clifton, K. J. (2009). Influence of individual perceptions and bicycle infrastructure on decision to bike. Transportation Research Record: Journal of the Transportation Research Board, 2140(1), 165–172. https://doi.org/10.3141/2140-18
  • Bachand-Marleau, J., Lee, B. H. Y., & El-Geneidy, A. M. (2012). Better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use. Transportation Research Record: Journal of the Transportation Research Board, 2314(1), 66–71. https://doi.org/10.3141/2314-09
  • bizGIS. (2019). bizGIS database. http://www.biz-gis.com
  • Bordagaray, M., dell’Olio, L., Fonzone, A., & Ibeas, Á. (2016). Capturing the conditions that introduce systematic variation in bike-sharing travel behavior using data mining techniques. Transportation Research Part C: Emerging Technologies, 71, 231–248. https://doi.org/10.1016/j.trc.2016.07.009
  • Borgnat, P., Abry, P., Flandrin, P., Robardet, C., Rouquier, J. B., & Fleury, E. (2011). Shared bicycles in a city: A signal processing and data analysis perspective. Advances in Complex Systems, 14(03), 415–438. https://doi.org/10.1142/S0219525911002950
  • Çetinkaya, C. (2017). Bike sharing station site selection for Gaziantep. Sigma: Journal of Engineering & Natural Sciences, 35(3), 535–543.
  • Croci, E., & Rossi, D. (2014). Optimizing the position of bike sharing stations. The Milan Case. SSRN Electronic Journal, 68, 1–36. https://doi.org/10.2139/ssrn.2461179
  • Curran, A. (2008). Public bike system – Feasibility study. TransLink public bike system feasibility study. PBS feasibility study. Quay Communications Inc.
  • Department for City Planning New York. (2009). Bike-share. Opportunities in New York City. New York, 142. http://www.nyc.gov/html/dcp/pdf/transportation/bike_share_complete.pdf
  • Dhingra, C., Kodukula, S. (2010). Public bicycle schemes: Applying the concept in developing cities examples from India sustainable urban transport. 38. http://www.sutp.org/files/contents/documents/resources/B_Technical-Documents/GIZ_SUTP_TD3_Public-Bicycle-Schemes_EN.pdf
  • Dill, J., & Voros, K. (2007). Factors affecting bicycling demand: Initial survey findings from the Portland, Oregon, region. Transportation Research Record: Journal of the Transportation Research Board, 2031(1), 9–17. https://doi.org/10.3141/2031-02
  • Ehrenfeucht, R., & Loukaitou-Sideris, A. (2010). Planning urban sidewalks: Infrastructure, daily life and destinations. Journal of Urban Design, 15(4), 459–471. https://doi.org/10.1080/13574809.2010.502333
  • Eren, E., & Uz, V. E. (2020). A review on bike-sharing: The factors affecting bike-sharing demand. Sustainable Cities and Society, 54, 101882. https://doi.org/10.1016/j.scs.2019.101882
  • Faghih-Imani, A., & Eluru, N. (2015). Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system. Journal of Transport Geography, 44, 53–64. https://doi.org/10.1016/j.jtrangeo.2015.03.005
  • Faghih-Imani, A., Eluru, N., El-Geneidy, A. M., Rabbat, M., & Haq, U. (2014). How land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal. Journal of Transport Geography, 41, 306–314. https://doi.org/10.1016/j.jtrangeo.2014.01.013
  • Feng, Y., Affonso, R. C., Marc, Z., Feng, Y., Affonso, R. C., Marc, Z., Feng, Y., Affonso, R. C., Zolghadri, M., & Hainaut, F. (2017). Analysis of bike sharing system by clustering: The Vélib’ case To cite this version: HAL Id: hal-01494490 Analysis of bike sharing system by clustering: the V´. Ifac.
  • Garrard, J., Rose, G., & Lo, S. K. (2008). Promoting transportation cycling for women: The role of bicycle infrastructure. Preventive Medicine, 46(1), 55–59. https://doi.org/10.1016/j.ypmed.2007.07.010
  • Griffin, G. P., & Sener, I. N. (2016). Public transit equity analysis at metropolitan and local scales: A focus on nine large cities in the US. Journal of Public Transportation, 19(4), 126–143. https://doi.org/10.5038/2375-0901.19.4.8
  • Habib, K. N., Mann, J., Mahmoud, M., & Weiss, A. (2014). Synopsis of bicycle demand in the City of Toronto: Investigating the effects of perception, consciousness and comfortability on the purpose of biking and bike ownership. Transportation Research Part A: Policy and Practice, 70, 67–80.
  • Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series c (Applied Statistics), 28(1), 100–108.
  • Heinen, E., van Wee, B., & Maat, K. (2010). Commuting by bicycle: An overview of the literature. Transport Reviews, 30(1), 59–96. https://doi.org/10.1080/01441640903187001
  • Hyland, M., Hong, Z., de Farias Pinto, H. K. R., & Chen, Y. (2018). Hybrid cluster-regression approach to model bikeshare station usage. Transportation Research Part A: Policy and Practice, 115, 71–89.
  • Jäppinen, S., Toivonen, T., & Salonen, M. (2013). Modelling the potential effect of shared bicycles on public transport travel times in Greater Helsinki: An open data approach. Applied Geography, 43, 13–24. https://doi.org/10.1016/j.apgeog.2013.05.010
  • Joshi, K. D., & Nalwade, P. S. (2013). Modified K-means for better initial cluster centres.
  • Kabak, M., Erbaş, M., Çetinkaya, C., & Özceylan, E. (2018). A GIS-based MCDM approach for the evaluation of bike-share stations. Journal of Cleaner Production, 201, 49–60. https://doi.org/10.1016/j.jclepro.2018.08.033
  • Kaltenbrunner, A., Meza, R., Grivolla, J., Codina, J., & Banchs, R. (2010). Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system. Pervasive and Mobile Computing, 6(4), 455–466. https://doi.org/10.1016/j.pmcj.2010.07.002
  • Kim, D., Shin, H., Im, H., & Park, J. (2012). Factors influencing travel behaviors in bikesharing. Transportation Research Board 91st Annual Meeting (pp. 1–14). Transportation Research Board.
  • Kim, K. J., & Ahn, H. (2008). A recommender system using GA K-means clustering in an online shopping market. Expert Systems with Applications, 34(2), 1200–1209. https://doi.org/10.1016/j.eswa.2006.12.025
  • Kumar, V., Chauhan, H., & Panwar, D. (2013). K-means clustering approach to analyze NSL-KDD intrusion detection dataset. International Journal of Soft Computing and Engineering, 3(4), 1–4.
  • Lin, J. R., & Yang, T. H. (2011). Strategic design of public bicycle sharing systems with service level constraints. Transportation Research Part E: Logistics and Transportation Review, 47(2), 284–294. https://doi.org/10.1016/j.tre.2010.09.004
  • Lu, W., Scott, D. M., & Dalumpines, R. (2018). Understanding bike share cyclist route choice using GPS data: Comparing dominant routes and shortest paths. Journal of Transport Geography, 71, 172–181. https://doi.org/10.1016/j.jtrangeo.2018.07.012
  • MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1(14), 281–297.
  • Mateo-Babiano, I., Bean, R., Corcoran, J., & Pojani, D. (2016). How does our natural and built environment affect the use of bicycle sharing? Transportation Research Part A: Policy and Practice, 94, 295–307.
  • Meddin, R., & DeMaio, P. (2019). The bike-sharing blog. http://bikesharing.blogspot.com/
  • Mete, S., Cil, Z. A., & Özceylan, E. (2018). Location and coverage analysis of bike-sharing stations in university campus. Business Systems Research Journal, 9(2), 80–95. https://doi.org/10.2478/bsrj-2018-0021
  • Midgley, P. (2009). The role of smart bike-sharing systems in urban mobility. Journeys, 2(1), 23–31.
  • Midgley, P. (2011). Bicycle-sharing schemes: Enhancing sustainable mobility in urban areas. Commission on Sustainable Development. Nine teenth session, 8, 24. http://www.un.org/esa/dsd/resources/res_pdfs/csd-19/Background-Paper8-P.Midgley-Bicycle.pdf
  • Ministry of Land, Infrastructure, and Transport. (2019). Seoul smart map. http://map.seoul.go.kr
  • National Geographic Information Institute. (2019). Geographic information platform. http://map.ngii.go.kr
  • National Spatial Data Infrastructure Portal. (2019). Korea national spatial data infrastructure portal. http://www.nsdi.go.kr
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory.
  • Olde Kalter, M. J. (2007). Vaker op de fiets? Effecten van overheidsmaatregelen (More often the bicycle? Effects of government measures). The Hague: Kennisinstituut voor Mobiliteitsbeleid [KiM].
  • Rahul, T. M., & Verma, A. (2014). A study of acceptable trip distances using walking and cycling in Bangalore. Journal of Transport Geography, 38, 106–113. https://doi.org/10.1016/j.jtrangeo.2014.05.011
  • Rixey, R. A. (2013). Station-level forecasting of bikesharing ridership: Station network effects in three US systems. Transportation Research Record: Journal of the Transportation Research Board, 2387(1), 46–55. https://doi.org/10.3141/2387-06
  • Sa, K., & Lee, S. (2018). Analysis of physical characteristics affecting the usage of public bike in Seoul, Korea - Focused on the different influences of factors by distance to bike station - (In Korean). Journal of Korea Planning Association, 53(6), 39–59. https://doi.org/10.17208/jkpa.2018.11.53.6.39
  • Schoner, J., & Levinson, D. M. (2013). Which station? Access trips and bike share route choice.
  • Seoul, Bike. (2019). Seoul bike offical site. https://www.bikeseoul.com/
  • Seoul Metropolitan Government. (2019). Seoul metropolitan government offical site. https://www.seoul.go.kr/
  • Seoul Open Data (2019). Seoul open database. http://data.seoul.go.kr
  • Shaheen, S., Guzman, S., & Zhang, H. (2010). Bikesharing in Europe, the Americas, and Asia. Transportation Research Record: Journal of the Transportation Research Board, 2143(1), 159–167. https://doi.org/10.3141/2143-20
  • Verheugen, G. (2005). European commission. Pharmaceuticals Policy and Law 6, 1–61. https://doi.org/10.4324/9781849776110-28
  • Vogel, P., Greiser, T., & Mattfeld, D. C. (2011). Understanding bike-sharing systems using data mining: Exploring activity patterns. Procedia - Social and Behavioral Sciences, 20, 514–523. https://doi.org/10.1016/j.sbspro.2011.08.058
  • Wang, K., Akar, G., & Chen, Y.-J. (2018). 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, 116, 1–14.
  • Whittaker, R. H. (1972). Evolution and measurement of species diversity. TAXON, 21(2-3), 213–251. https://doi.org/10.2307/1218190
  • Xu, H., Ying, J., Lin, F., & Yuan, Y. (2013). Station segmentation with an improved K-means algorithm for Hangzhou Public Bicycle System. Journal of Software, 8(9), 2289–2296. https://doi.org/10.4304/jsw.8.9.2289-2296
  • Xu, S. J., & Chow, J. Y. J. (2020). A longitudinal study of bike infrastructure impact on bikesharing system performance in New York City. International Journal of Sustainable Transportation, 14(11), 886–902. https://doi.org/10.1080/15568318.2019.1645921
  • Yang, Y., Heppenstall, A., Turner, A., & Comber, A. (2020). Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems. Computers, Environment and Urban Systems, 83(July), 101521. https://doi.org/10.1016/j.compenvurbsys.2020.101521
  • Zhang, Y., Brussel, M. J. G., Thomas, T., & van Maarseveen, M. F. A. M. (2018). Mining bike-sharing travel behavior data: An investigation into trip chains and transition activities. Computers, Environment and Urban Systems, 69, 39–50. https://doi.org/10.1016/j.compenvurbsys.2017.12.004
  • Zhu, R., Zhang, X., Kondor, D., Santi, P., & Ratti, C. (2020). Understanding spatio-temporal heterogeneity of bike-sharing and scooter-sharing mobility. Computers, Environment and Urban Systems, 81, 101483. https://doi.org/10.1016/j.compenvurbsys.2020.101483
  • Zhuang, D., Jin, J. G., Shen, Y., & Jiang, W. (2021). Understanding the bike sharing travel demand and cycle lane network: The case of Shanghai. International Journal of Sustainable Transportation, 15(2), 111–113. https://doi.org/10.1080/15568318.2019.1699209

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