Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in Nanjing

Authors

  • Cheng Lyu Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, China
  • Xinhua Wu Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, China
  • Yang Liu Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, China
  • Zhiyuan Liu Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, China
  • Xun Yang Southeast University

DOI:

https://doi.org/10.5198/jtlu.2020.1568

Keywords:

Public bicycle system, Built environment, Multi-scale geographically weighted regression, Spatial heterogeneity

Abstract

A public bicycle system (PBS) is a promising countermeasure for the traffic issues induced by rapid urbanization, and it is widely acknowledged that the built environment has a significant impact on the use of a PBS. However, as the urban built-up area expands, different regions within a city can exhibit diverse characteristics. The spatial effects and differences among regions have been neglected by existing studies. To better understand how the urban built environment affects PBS ridership, this study conducts a quantitative analysis of the spatial relationship. It introduces a multi-scale geographically weighted regression (MGWR) to accomplish this task and conducts and evaluates a case study of the PBS in Nanjing, China. Six types of “D” variables (density, diversity, design, destination accessibility, distance to transit, and demand management) are involved in the analysis. The proposed method outperforms linear regression and standard geographically weighted regression (GWR) in terms of explanatory power. The modeling results demonstrate different influencing patterns between traditional downtown areas and newly built-up areas, especially for the density of population, road network, parking space, and various points of interest.

References

Amap. (2016). Amap JavaScript API (Version 1.3). Amap. Retrieved from https://lbs.amap.com/api/javascript-api/summary

Anselin, L. (1995). Local indicators of spatial association-LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x

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

Bhat, C., & Zhao, H. (2002). The spatial analysis of activity stop generation. Transportation Research Part B: Methodological, 36(6), 557–575. https://doi.org/10.1016/S0191-2615(01)00019-4

Boarnet, M. G., & Crane, R. (2001). Travel by design: The influence of urban form on travel. Oxford, England: Oxford University Press.

Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x

Buck, D., & Buehler, R. (2011, November 15). Bike lanes and other determinants of capital bikeshare trips. Paper presented at the Transportation Research Board 91st Annual Meeting, Washington, DC.

Cervero, R. (2002). Induced travel demand: Research design, empirical evidence, and normative policies. Journal of Planning Literature, 17(1), 3–20. https://doi.org/10.1177/088122017001001

Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199–219. https://doi. org/10.1016/S1361-9209(97)00009-6

de Souza, F., La Paix Puello, L., Brussel, M., Orrico, R., & van Maarseveen, M. (2017). Modelling the potential for cycling in access trips to bus, train and metro in Rio de Janeiro. Transportation Research Part D: Transport and Environment, 56, 55–67. https://doi. org/10.1016/j.trd.2017.07.007

DeMaio, P., & Meddin, R. (2019). The bike-sharing world map. Retrieved from www.bikesharingmap.com

Ding, C., Wang, D., Liu, C., Zhang, Y., & Yang, J. (2017). Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance. Transportation Research Part A: Policy and Practice, 100, 65–80. https://doi.org/10.1016/j.tra.2017.04.008

Ding, C., Wang, Y., Yang, J., Liu, C., & Lin, Y. (2016). Spatial heterogeneous impact of built environment on household auto ownership levels: Evidence from analysis at traffic analysis zone scales. Transportation Letters, 8(1), 26–34. https://doi.org/10.1179/1942787515Y.0000000004

El-Assi, W., Salah Mahmoud, M., & Nurul Habib, K. (2017). Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation, 44(3), 589–613. https://doi.org/10.1007/s11116-015-9669-z

Ewing, R., & Cervero, R. (2010). Travel and the built environment: A meta-analysis. Journal of the American Planning Association, 76(3), 265–294. doi.org/10.1080/01944361003766766

Faghih-Imani, A., & Eluru, N. (2016). Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system. Journal of Transport Geography, 54, 218–227. https://doi.org/10.1016/j.jtrangeo.2016.06.008

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

Faghih-Imani, A., Hampshire, R., Marla, L., & Eluru, N. (2017). An empirical analysis of bike sharing usage and rebalancing: Evidence from Barcelona and Seville. Transportation Research Part A: Policy and Practice, 97, 177–191. https://doi.org/10.1016/j.tra.2016.12.007

Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247–1265. https://doi.org/10.1080/24694452.2017.1352480

Frade, I., & Ribeiro, A. (2014). Bicycle sharing systems demand. Procedia–Social and Behavioral Sciences, 111, 518–527. https://doi.org/10.1016/j.sbspro.2014.01.085

Gebhart, K., & Noland, R. B. (2014). The impact of weather conditions on bikeshare trips in Washington, DC. Transportation, 41(6), 1205–1225. https://doi.org/10.1007/s11116-014-9540-7

Handy, S. L., Boarnet, M. G., Ewing, R., & Killingsworth, R. E. (2002). How the built environment affects physical activity. American Journal of Preventive Medicine, 23(2), 64–73. https://doi.org/10.1016/S0749-3797(02)00475-0

Huang, K., Liu, Z., Zhu, T., Kim, Y., & An, K., (2019). Analysis of the acceptance of park-and-ride by users: A cumulative logistic regression approach. Journal of Transport and Land Use, 12(1) 637–647. https://doi.org/10.5198/jtlu.2019.1390

Kager, R., Bertolini, L., & Te Brömmelstroet, M. (2016). Characterization of and reflections on the synergy of bicycles and public transport. Transportation Research Part A: Policy and Practice, 85, 208–219. https://doi.org/10.1016/j.tra.2016.01.015

Kutela, B., & Teng, H. (2019). The influence of campus characteristics, temporal factors, and weather events on campuses-related daily bike-share trips. Journal of Transport Geography, 78, 160–169. https://doi.org/10.1016/j.jtrangeo.2019.06.002

Lin, J.-J., Zhao, P., Takada, K., Li, S., Yai, T., & Chen, C.-H. (2018). Built environment and public bike usage for metro access: A comparison of neighborhoods in Beijing, Taipei, and Tokyo. Transportation Research Part D: Transport and Environment, 63, 209–221. https://doi.org/10.1016/j.trd.2018.05.007

Liu, Z., Chen, X., Meng, Q., & Kim, I. (2018). Remote park-and-ride network equilibrium model and its applications. Transportation Research Part B: Methodological, 117, 37–62. https://doi.org/10.1016/j.trb.2018.08.004

Liu, Y., Jia, R., Xie, X., & Liu, Z. (2019). A two-stage destination prediction framework of shared bicycle based on geographical position recommendation. IEEE Intelligent Transportation Systems Magazine, 11(1), 42–47. https://doi.org/10.1109/MITS.2018.2884517

Liu, Z., Wang, S., Zhou, B., & Cheng, Q., (2017). Robust optimization of distance-based tolls in a network considering stochastic day to day dynamics. Transportation Research Part C, 79, 58–72.

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. https://doi.org/10.1016/j.tra.2016.09.015

Nanjing Municipal Bureau of Statistics. (2017). Statistical yearbook of Nanjing.

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. https://doi.org/10.1016/j.jtrangeo.2013.06.007

Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2018). MGWR: A python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. OSF Preprints. https://doi.org/10.31219/osf.io/bphw9

Qian, X., & Ukkusuri, S. V. (2015). Spatial variation of the urban taxi ridership using GPS data. Applied Geography, 59, 31–42. https://doi.org/10.1016/j.apgeog.2015.02.011

Raux, C., Zoubir, A., & Geyik, M. (2017). Who are bike sharing schemes members and do they travel differently? The case of Lyon’s “Velo’v” scheme. Transportation Research Part A: Policy and Practice, 106, 350–363. https://doi.org/10.1016/j.tra.2017.10.010

Schoner, J., & Levinson, D. M. (2013). Which station? Access trips and bike share route choice (Working Papers No. 000117; Nexus Working Papers, pp. 15). Minneapolis: Center for Transportation Studies.

Shaheen, S., Guzman, S., & Zhang, H. (2010). Bikesharing in Europe, the Americas, and Asia: Past, present, and future. Transportation Research Record: Journal of the Transportation Research Board, 2143, 159–167. https://doi.org/10.3141/2143-20

Shaheen, S., & Rodier, C. J. (2018). EasyConnect: Low-speed modes linked to transit planning project (UCD-ITS-RR-08-33). Berkeley, CA: Institute of Transportation Studies, University of California.

Tran, T. D., Ovtracht, N., & d’Arcier, B. F. (2015). Modeling bike sharing system using built environment factors. Procedia CIRP, 30, 293–298. https://doi.org/10.1016/j.procir.2015.02.156

Wang, Y., Liu, Y., Ji, S., Hou, L., Han, S. S., & Yang, L. (2018). Bicycle lane condition and distance: Case study of public bicycle system in Xi’an, China. Journal of Urban Planning and Development, 144(2), 05018001. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000436

Yang, X.-H., Cheng, Z., Chen, G., Wang, L., Ruan, Z.-Y., & Zheng, Y.-J. (2018). The impact of a public bicycle-sharing system on urban public transport networks. Transportation Research Part A: Policy and Practice, 107, 246–256. https://doi.org/10.1016/j.tra.2017.10.017

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

Zhang, Y., Thomas, T., Brussel, M., & van Maarseveen, M. (2017). 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, 58, 59–70. https://doi.org/10.1016/j.jtrangeo.2016.11.014

Zhao, J., Deng, W., & Song, Y. (2014). 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, 253–264. https://doi.org/10.1016/j.tranpol.2014.06.008

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Published

2020-11-12

How to Cite

Lyu, C., Wu, X., Liu, Y., Liu, Z., & Yang, X. (2020). Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in Nanjing. Journal of Transport and Land Use, 13(1), 447-467. https://doi.org/10.5198/jtlu.2020.1568

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Section

Special Issue: Innovations for Transport Planning in China