OPEN ACCESS
Public bike systems have grown in popularity and are expanding rapidly across cities worldwide. Such systems provide access to pickup and drop-off public bikes at numerous bike stations for free or for an affordable fee and aim at increasing bike use and extending the accessibility of traditional public transport systems. A variety of studies have examined the characteristics of bike-sharing systems, mostly in American and European cities and with a focus on user demographics. The objective of this study is to investigate the general characteristics of system usage, in terms of system efficiency, trip characteristics and bike activity patterns, for Zhongshan’s public bike system during a five-month period. The findings
show that the system is not very efficient based on usage metrics which are low compared to successful systems in other countries. Demand is relatively high in city centre zones due to high population and activity density. However, there is no clear direction of inbound or outbound trips in rush hours. This may be attributed not only to mixed land use patterns throughout the city, but also to the fact that most trips are local trips over short distance. This could indicate that public bike trips are mainly substitute for walking trips rather than for car or PT trips. On the outskirts, demand and system efficiency are low, indicating that location allocation of stations needs adjustment. In the conclusions, we discuss how these findings can be used for improving the system.
bike-sharing, bike activity patterns, system efficiency, trip characteristics
[1] liu, Z., Jia, x. & cheng, W., Solving the last mile problem: ensure the success of public bicycle system in beijing. Procedia – Social and Behavioral Sciences, 43, pp. 73–78, 2012. DOI: 10.1016/j.sbspro.2012.04.079.
[2] Shaheen, S., guzman, S. & Zhang, h., bikesharing in europe, the americas, and asia Past, Present, and future. Transportation Research Record, 2143, pp. 159–167, 2010. DOI: 10.3141/2143-20.
[3] O’brien, O., cheshire, J. & batty, m., mining bicycle sharing data for generating insights into sustainable transport systems. Journal of Transport Geography, 34, pp. 262–273, 2014. DOI: 10.1016/j.jtrangeo.2013.06.007.
[4] ITDP-china, http://www.publicbike.net/defaulten.aspx
[5] Shaheen, S., Zhang, h., martin, e. & guzman, S., hangzhou public bicycle: understanding early adoption and behavioral response to bikesharing in hangzhou, china. Transportation Research Record, 2247, pp. 34–41, 2011. DOI: 10.3141/2247-05.
[6] bachand-marleau, J., lee, b. & el-geneidy, a., better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use. Transportation Research Record, 2314, pp. 66–71, 2012. DOI: 10.3141/2314-09.
[7] Parkes, S.D., marsden, g., Shaheen, S.a. & cohen, a.P., understanding the diffusion of public bikesharing systems: evidence from europe and North america. Journal of Transport Geography, 31, pp. 94–103, 2013. DOI: 10.1016/j.jtrangeo.2013.06.003.
[8] froehlich, J., Neumann, J. & Oliver, N., Sensing and predicting the pulse of the city through shared bicycling. Proceedings of 21st International Joint Conference on Artificial intelligence, eds. Pasadena, california, uSa, pp. 1420–1426, 2009.
[9] kaltenbrunner, a., meza, r., grivolla, J., codina, J. & banchs, r., urban cycles and mobility patterns: exploring and predicting trends in a bicycle-based public transport system. Pervasive and Mobile Computing, 6, pp. 455–466, 2010. DOI: 10.1016/j. pmcj.2010.07.002.
[10] Vogel, P., greiser, T. & mattfeld, D.c., understanding bike-sharing systems using data mining: exploring activity patterns. Procedia – Social and Behavioral Sciences, 20, pp. 514–523, 2011. DOI: 10.1016/j.sbspro.2011.08.058.
[11] borgnat, P., abry, P., flandrin, P., robardet, c., rouquier, J.b. & fleury, e., Shared bicycles in a city: a signal processing and data analysis perspective. Advances in Complex Systems, 14, pp. 415–438, 2011. DOI: 10.1142/S0219525911002950.
[12] Jensen, P., rouquier, J.-b., Ovtracht, N. & robardet, c., characterizing the speed and paths of shared bicycle use in lyon. Transportation Research Part D: Transport and Environment, 15, pp. 522–524, 2010. DOI: 10.1016/j.trd.2010.07.002.
[13] Jäppinen, S., Toivonen, T. & Salonen, m., modelling the potential effect of shared bicycles on public transport travel times in greater helsinki: an open data approach. Applied Geography, 43, pp. 13–24, 2013. DOI: 10.1016/j.apgeog.2013.05.010.
[14] Zhao, J., Wang, J. & Deng, W., exploring bikesharing travel time and trip chain by gender and day of the week. Transportation Research Part C: Emerging Technologies, 58( Part B), pp. 251–264, 2015. DOI: 10.1016/j.trc.2015.01.030.
[15] fuller, D., Sahlqvist, S., cummins, S. & Ogilvie, D., The impact of public transportation strikes on use of a bicycle share program in london: Interrupted time series design. Preventive Medicine, 54, pp. 74–76, 2012. DOI: 10.1016/j.ypmed.2011.09.021.
[16] lathia, N., ahmed, S. & capra, l., measuring the impact of opening the london shared bicycle scheme to casual users. Transportation Research Part C: Emerging Technologies, 22, pp. 88–102, 2012. DOI: 10.1016/j.trc.2011.12.004.
[17] Zhao, J., Deng, W. & Song, y., ridership and effectiveness of bikesharing: The effects of urban features and system characteristics on daily use and turnover rate of public bikes in china. Transport Policy, 35, pp. 253–264, 2014. DOI: 10.1016/j.tranpol.2014.06.008.
[18] gauthier, a., hughes, c., kost, c., li, S., linke, c., lotshaw, S., mason, J., Pardo, c., rasore, c., Schroeder, b. & Treviño, x., ITDP Bike-Share Planning Guide, ITDP: New york, p 152, 2013.
[19] Zhang, h., Shaheen, S.a. & chen, x.P., bicycle evolution in china: from the 1900s to the present. International Journal of Sustainable Transportation, 8, pp. 317–335, 2014. DOI: 10.1080/15568318.2012.699999.
[20] Norusis, m.J., cluster analysis (chapter 17). IBM SPSS Statistics 19 Statistical Procedures Companion, Prentice hall: New Jersey, pp. 375–404, 2011.