Understanding the spatiotemporal patterns of public bicycle usage: A case study of Hangzhou, China

References

• Anselin, L., & Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. In A. Ullah & D. E. A. Giles (Eds.), Handbook of applied economic statistics (pp. 237–289). New York, NY: Marcel Dekker.
   Google Scholar
• Anselin, L., Bera, A. K., Florax, R., & Yoon, M. J. (1996). Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26(1), 77–104.
   Web of Science ®Google Scholar
• Bavaud, F. (1998). Models for spatial weights: A systematic look. Geographical Analysis, 30(2), 153–171.
   Web of Science ®Google Scholar
• Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. New York, NY: Plenum Press.
   Google Scholar
• 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.
   Google Scholar
• Calabrese, F., Diao, M., Di Lorenzo, G., Ferreira, J., & Ratti, C. (2013). Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation Research Part C: Emerging Technologies, 26, 301–313.
   Web of Science ®Google Scholar
• Cavill, N., Kahlmeier, S., & Racioppi, F. (2006). Physical activity and health in Europe: Evidence for action. Europe: Publication of World Health Organization. Retrieved from http://www.euro.who.int/__data/assets/pdf_file/0011/87545/E89490.pdf
   Google Scholar
• Chen, Y. (2009). Analysis on inhabitant trip characteristic and discussion on traffic development countermeasure. Urban Roads, Bridges & Flood Control, 12, 44–48 (in Chinese).
   Google Scholar
• DeMaio, P. (2009). Bicycle-sharing: History, impacts, models of provision, and future. Journal of Public Transportation, 12(4), 41–56.
   Google Scholar
• Dembele, D., & Kastner, P. (2003). Fuzzy C-means method for clustering microarray data. Bioinformatics (Oxford, England), 19(8), 973–980.
   PubMed Web of Science ®Google Scholar
• Diao, M., Zhu, Y., Ferreira, J., & Ratti, C. (2016). Inferring individual daily activities from mobile phone traces: A Boston example. Environment and Planning B: Planning and Design, 43(5), 920–940.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Faghih-Imani, A., & Eluru, N. (2016a). Determining the role of bicycle sharing system infrastructure installation decision on usage: Case study of Montreal BIXI system. Transportation Research Part A: Policy and Practice, 94, 685–698.
   Web of Science ®Google Scholar
• Faghih-Imani, A., & Eluru, N. (2016b). Incorporating the impact of spatiotemporal interactions on bicycle sharing system demand: A case study of New York CitiBike system. Journal of Transport Geography, 54, 218–227.
   Web of Science ®Google Scholar
• 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, 306–324.
   Web of Science ®Google Scholar
• Fishman, E., Washington, S., & Haworth, N. (2013). Bike share: A synthesis of the literature. Transport Reviews, 33(2), 148–165.
   Web of Science ®Google Scholar
• Froehlich, J., Neumann, J., & Oliver, N. (2008). Measuring the pulse of the city through shared bicycle programs. Paper presented at the Proceedings of UrbanSense08, Raleigh, NC, pp. 16–20.
   Google Scholar
• Futschik, M. E., & Carlisle, B. (2005). Noise-robust soft clustering of gene expression time-course data. Journal of Bioinformatics and Computational Biology, 3(4), 965–988.
   PubMedGoogle Scholar
• Gebhart, K., & Noland, R. (2014). The impact of weather conditions on bikeshare trips in Washington, DC. Transportation, 41(6), 1205–1225.
   Web of Science ®Google Scholar
• Hampshire, R. C., & Marla, L. (2012). An analysis of bike sharing usage: Explaining trip generation and attraction from observed demand. Paper presented at the Transportation Research Board 91st Annual Meeting (No. 12-2099). https://trid.trb.org/view.aspx?id=1129620
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Kumar, L., & Futschik, M. E. (2007). Mfuzz: A software package for soft clustering of microarray data. Bioinformation, 2(1), 5–7.
   PubMedGoogle Scholar
• Liao, W. T. (2005). Clustering of time series data—A survey. Pattern Recognition, 38(11), 1857–1874.
   Web of Science ®Google Scholar
• Lu, X., & Gu, X. (2011). The fifth travel survey of residents in Shanghai and characteristics analysis. Urban Transport of China, 9(5), 1–7. (In Chinese, with English titles).
   Google Scholar
• Midgley, P. (2011). Bicycle-sharing schemes: Enhancing sustainable mobility in urban areas. New York, NY: United Nations. Retrieved from http://www.un.org/esa/dsd/resources/res_pdfs/csd-19/Background-Paper8-P.Midgley-Bicycle.pdf
   Google Scholar
• Murphy, H. (2010). Dublin bikes: An investigation in the context of multimodal transport. Dublin: MSc Sustainable Development, Dublin Institute of Technology.
   Google Scholar
• Nadal, L. (2007). Bike sharing sweeps Paris off its feet. Sustainable Transport, 19, 8–12.
   Google Scholar
• Nair, R., Miller-Hooks, E., Hampshire, R. C., & Bušić, A. (2013). Large-scale vehicle sharing systems: Analysis of Vélib'. International Journal of Sustainable Transportation, 7(1), 85–106.
   Web of Science ®Google Scholar
• O’Brien, O., Cheshire, J., & Batty, M. (2014). Mining bicycle sharing data for generating insights into sustainable transport systems. Journal of Transport Geography, 34, 262–273.
   Web of Science ®Google Scholar
• Rezaee, M. R., Lelieveldt, B. P. F., & Reiber, J. H. C. (1998). A new cluster validity index for the fuzzy c-mean. Pattern Recognition Letters, 19(3–4), 237–246.
   Google Scholar
• Rixey, R. (2013). Station-level forecasting of bikesharing ridership: Station network effects in three U.S., Systems. Transportation Research Record: Journal of the Transportation Research Board, 2387(1), 46–55.
   Google Scholar
• Romero, C. (2008). SpiCycles in Barcelona. Presented to Chamber of Commerce and Industry, December 19, in Bucharest, Romania. Retrieved from http://spicycles.velo.info/Portals/0/FinalReports/Barcelona_Final_Report.ppt
   Google Scholar
• Rudloff, C., & Lackner, B. (2014). Modeling demand for bike sharing systems: Neighboring stations as source for demand and reason for structural breaks. Transportation Research Record: Journal of the Transportation Research Board, 2430(1), 1–11.
   Google Scholar
• Schwämmle, V., & Jensen, O. N. (2010). A simple and fast method to determine the parameters for fuzzy c–means cluster analysis. Bioinformatics, 26(22), 2841–2848.
   PubMed Web of Science ®Google Scholar
• Shaheen, S., Martin, E., Cohen, A. P., & Finson, R. (2012). Public bikesharing in North America: Early operator and user understanding. San Jose, CA: San Jose State University.
   Google Scholar
• Shaheen, S. A., Guzman, S., & Zhang, H. (2010). Bicycle sharing in Europe, the America, and Asia. Transportation Research Record: Journal of the Transportation Research Board, 2143(1), 159–167.
   Web of Science ®Google Scholar
• Shen, Y., Zhang, X., & Zhao, J. (2018). Understanding the usage of dockless bike sharing in Singapore. International Journal of Sustainable Transportation, 12(9), 686–700.
   Google Scholar
• Shi, X. F., Cui, D. X., & Wei, W. (2011). Investigation of the relationship of using and planning of public bicycle in Hangzhou. Urban Development Studies, 10, 105–114. (in Chinese)
   Google Scholar
• Tang, Y., Pan, H., & Shen, Q. (2011). Bike-sharing systems in Beijing, Shanghai and Hangzhou and their impact on travel behaviour. Paper presented at the Transportation Research Board 90th Annual Meeting (No. 11-3862). https://trid.trb.org/view/1093278
   Google Scholar
• Tran, D., & Wagner, M. (2002). Fuzzy c-means clustering-based speaker verification. In N. R. Pal & M. Sugeno (Eds.), Advances in soft computing—AFSS 2002 (pp. 318–324). Heidelberg, Germany: Springer Berlin Heidelberg.
   Google Scholar
• Wang, X. (2010). Discussion of public bicycle operation model in the city of Hangzhou. Transportation Enterprise Management, 25(5), 2010. (in Chinese).
   Google Scholar
• Wang, X., Lindsey, G., Schoner, J., & Harrison, A. (2015). Modeling bike sharing station activity: Effects of nearby business and jobs on trip to and from stations. Journal of Urban Planning and Development, 142(1), 1–9.
   Web of Science ®Google Scholar
• Wilson, N., Zhao, J., & Rahbee, A. (2009). The potential impact of automated data collection systems on urban public transport planning. In N. Wilson & A. Nuzzolo (Eds.), Schedule-based modeling of transportation networks: Theory and applications. New York, NY: Springer.
   Google Scholar
• Woodcock, J., Tainio, M., Cheshire, J., O’Brien, O., & Goodman, A. (2014). Health effects of the London bicycle sharing system: Health impact modelling study. British Medical Journal, 348, 425.
   PubMed Web of Science ®Google Scholar
• Zhu, W., Pang, Y., Wang, D., & Timmermans, H. (2013). Travel behavior change after the introduction of public bicycle systems: Case study in Minhang District, Shanghai. Paper presented at the Transportation Research Board 92nd Annual Meeting (No. 13-0764). https://trid.trb.org/view/1240650
   Google Scholar