Measuring bicycle accessibility within the metro catchment area: An empirical study in Shanghai
Yizhe Huang
Ningbo University of Technology and Zhejiang Engineering Research Center of Digital Road Construction Technology
Chaobo Shi
Ningbo University of Technology and Zhejiang Engineering Research Center of Digital Road Construction Technology
Cunzhuo Liu
Ningbo University of Technology and Zhejiang Engineering Research Center of Digital Road Construction Technology
Shuichao Zhang
Ningbo University of Technology and Zhejiang Engineering Research Center of Digital Road Construction Technology
DOI: https://doi.org/10.5198/jtlu.2026.2694
Keywords: accessibility, bicycle-metro integration, multiple data sources, metro catchment area, bicycle suitability
Abstract
Accessibility describes the potential to reach opportunities and is widely used to assess the ease of reaching destinations through urban transport systems. Although much attention has been given to investigate bicycle-accessibility and metro-accessibility methods, extending these methods to model bicycle-metro integration travel at the city scale remains challenging. Based on Hansen’s accessibility model, this study proposes three different models to measure bicycle accessibility within metro catchment areas. In particularly, key factors such as trip purposes, bicycle suitability, total travel time, and traffic demand are incorporated into the accessibility models. These proposed models have been tested and compared using empirical data from Shanghai. Overall, metro stations with multiple interchange lines, cycling-friendly facilities and diverse surrounding activities tend to exhibit higher bicycle accessibility, particularly those located in the city center. For areas with low bicycle accessibility in the city, such as Baoshan Road Station and Anshan Xincun Station, targeted improvement measures can be implemented to enhance bicycle-metro integration and bicycle accessibility.
References
Bi, H., Gao, H., Li, A., & Ye, Z. (2024). Using topic modeling to unravel the nuanced effects of built environment on bicycle-metro integrated usage. Transportation Research Part A: Policy and Practice, 185, 104120. https://doi.org/10.1016/j.tra.2024.104120
Chen, C. F., & Chen, P. C. (2013). Estimating recreational cyclists’ preferences for bicycle routes—Evidence from Taiwan. Transport Policy, 26, 23–30. https://doi.org/10.1016/j.tranpol.2012.01.001
Du, Q., Zhou, Y., Huang, Y., Wang, Y., & Bai, L. (2022). Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership. Journal of Transport Geography, 102, 103380. https://doi.org/10.1016/j.jtrangeo.2022.103380
Florindo, A. A., Barrozo, L. V., Turrell, G., Barbosa, J. P. D. A. S., Cabral-Miranda, W., Cesar, C. L. G., & Goldbaum, M. (2018). Cycling for transportation in Sao Paulo City: Associations with bike paths, train and subway stations. International Journal of Environmental Research and Public Health, 15(4), 562. https://doi.org/10.3390/ijerph15040562
Fu, C., Huang, Z., Scheuer, B., Lin, J., & Zhang, Y. (2023). Integration of dockless bike-sharing and metro: Prediction and explanation at origin-destination level. Sustainable Cities and Society, 99, 104906. https://doi.org/10.1016/j.scs.2023.104906
Gehrke, S. R., Akhavan, A., Furth, P. G., Wang, Q., & Reardon, T. G. (2020). A cycling-focused accessibility tool to support regional bike network connectivity. Transportation Research Part D: Transport and Environment, 85, 102388. https://doi.org/10.1016/j.trd.2020.102388
Hansen, W. G. (1959). How accessibility shapes land use. Journal of the American Institute of Planners, 25(2), 73–76. https://doi.org/10.1080/01944365908978307
Harkey, D. L., Reinfurt, D. W., & Knuiman, M. (1998). Development of the bicycle compatibility index. Transportation Research Record, 1636(1), 13–20. https://doi.org/10.3141/1636-03
Hu, S., Chen, M., Jiang, Y., Sun, W., & Xiong, C. (2022). Examining factors associated with bike-and-ride (BnR) activities around metro stations in large-scale dockless bikesharing systems. Journal of Transport Geography, 98, 103271. https://doi.org/10.1016/j.jtrangeo.2021.103271
Iacono, M., Krizek, K. J., & El-Geneidy, A. (2010). Measuring non-motorized accessibility: Issues, alternatives, and execution. Journal of Transport Geography, 18(1), 133–140. https://doi.org/10.1016/j.jtrangeo.2009.02.002
Jin, S. T., & Sui, D. Z. (2024). Bikesharing and equity: A nationwide study of bikesharing accessibility in the U.S. Transportation Research Part A: Policy and Practice, 181. https://doi.org/10.1016/j.tra.2024.103983
Jones, E. G., & Carlson, T. D. (2003). Development of bicycle compatibility index for rural roads in Nebraska. Transportation Research Record, 1828(1), 124–132. https://doi.org/10.3141/1828-15
Kager, R., & Harms, L. (2017). Synergies from improved cycling-transit integration: Towards an integrated urban mobility system. International Transport Forum Discussion Paper. https://doi.org/10.1787/ce404b2e-en
Kazemzadeh, K., Laureshyn, A., Winslott Hiselius, L., & Ronchi, E. (2020). Expanding the scope of the bicycle level-of-service concept: A review of the literature. Sustainability, 12(7), 2944. https://doi.org/10.3390/su12072944
Kölbl, R., & Helbing, D. (2003). Energy laws in human travel behaviour. New Journal of Physics, 5(1), 48. https://doi.org/10.1088/1367-2630/5/1/348
Landis, B. W., Vattikuti, V. R., & Brannick, M. T. (1997). Real-time human perceptions: Toward a bicycle level of service. Transportation Research Record, 1578(1), 119–126. https://doi.org/10.3141/1578-15
Li, Z., Wang, W., Zhang, Y., Lu, J., & Ragland, D. R. (2012). Exploring factors influencing bicyclists’ perception of comfort on bicycle facilities 2. Facilities, 2(3), 4.
Li, Z. (2018). The impact of metro accessibility on residential property values: An empirical analysis. Research in Transportation Economics, 70, 52–56. https://doi.org/10.1016/j.retrec.2018.07.006
Li, A., Huang, Y., & Axhausen, K. W. (2020). An approach to imputing destination activities for inclusion in measures of bicycle accessibility. Journal of Transport Geography, 82, 102566. https://doi.org/10.1016/j.jtrangeo.2019.102566
Lin, D., Zhang, Y., & Meng, L. (2023). Assessing bike accessibility to metro systems by integrating crowdedness. Transactions in Urban Data, Science, and Technology, 2(4), 159–177. https://doi.org/10.1177/27541231231179403
Liu, J., He, M., Schonfeld, P. M., Kato, H., & Li, A. (2022). Measures of accessibility incorporating time reliability for an urban rail transit network: A case study in Wuhan, China. Transportation Research Part A: Policy and Practice, 165, 471–489. https://doi.org/10.1016/j.tra.2022.09.011
Liu, P., Chen, J., Sun, H., Guo, X., Wang, Y., & Zhu, Z. (2021). Assessing accessibility of dockless sharing‐bike networks by the social network analysis method. Journal of Advanced Transportation, 2021(1), 5584008. https://doi.org/10.1155/2021/5584008
Liu, X., Fan, J., Li, Y., Shao, X., & Lai, Z. (2022). Analysis of integrated uses of dockless bike sharing and ridesourcing with metros: A case study of Shanghai, China. Sustainable Cities and Society, 82, 103918. https://doi.org/10.1016/j.scs.2022.103918
Lowry, M. B., Callister, D., Gresham, M., & Moore, B. (2012). Assessment of communitywide bikeability with bicycle level of service. Transportation Research Record, 2314(1), 41–48. https://doi.org/10.3141/2314-06
Ma, J., Zheng, C., Yu, M., Shen, J., Zhang, H., & Wang, Y. (2024). The analysis of spatio-temporal characteristics and determinants of dockless bike-sharing and metro integration. Transportation Letters, 16(2), 182–195. https://doi.org/10.1080/19427867.2023.2170493
Nordback, K. (2014). Measuring traffic reduction from bicycle commuting. Transportation Research Record, 2468(1), 1–10. https://doi.org/10.3141/2468-11
Rahman, M. S. U. (2020). Public bike-sharing schemes (PBSS): Prospects in Bangladesh. Transportation Research Part A: Policy and Practice, 142, 207–224. https://doi.org/10.1016/j.tra.2020.09.022
Rosas-Satizábal, D., Guzman, L. A., & Oviedo, D. (2020). Cycling diversity, accessibility, and equality: An analysis of cycling commuting in Bogotá. Transportation Research Part D: Transport and Environment, 88, 102562. https://doi.org/10.1016/j.trd.2020.102562
Saghapour, T., Moridpour, S., Thompson, R. G. (2017). Measuring cycling accessibility in metropolitan areas. International Journal of Sustainable Transportation, 11(5), 381–394. https://doi.org/10.1080/15568318.2016.1262927
Schneider, F., Jensen, A. F., Daamen, W., & Hoogendoorn, S. (2023). Empirical analysis of cycling distances in three of Europe’s most bicycle-friendly regions within an accessibility framework. International Journal of Sustainable Transportation, 17(7), 775–789. https://doi.org/10.1080/15568318.2022.2095945
Shao, R., Derudder, B., & Yang, Y. (2022). Metro accessibility and space-time flexibility of shopping travel: A propensity score matching analysis. Sustainable Cities and Society, 87, 104204. https://doi.org/10.1016/j.scs.2022.104204
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. https://doi.org/10.1080/15568318.2018.1429696
Sun, Y., Mobasheri, A., Hu, X., & Wang, W. (2017). Investigating impacts of environmental factors on the cycling behavior of bicycle-sharing users. Sustainability, 9(6), 1060. https://doi.org/10.3390/su9061060
Transportation Research Board. (2000). Highway capacity manual. Transportation Research Board.
Turner, S. M., Shafer, C. S., & Stewart, W. P. (1997). Bicycle suitability criteria: Literature review and state-of-the-practice survey. Texas Transportation Institute.
Vale, D. S., Saraiva, M., & Pereira, M. (2016). Active accessibility: A review of operational measures of walking and cycling accessibility. Journal of Transport and Land Use, 9(1), 209–235. https://doi.org/10.5198/jtlu.2015.593
Van der Meer, L., Werner, C., & Loidl, M. (2024). Assessment of bicycle accessibility to mobility hubs under different criteria for cycling network quality. AGILE: GIScience Series, 5, 48. https://doi.org/10.5194/agile-giss-5-48-2024
Wang, R., Lu, Y., Wu, X., Liu, Y., & Yao, Y. (2020). Relationship between eye-level greenness and cycling frequency around metro stations in Shenzhen, China: A big data approach. Sustainable Cities and Society, 59, 102201. https://doi.org/10.1016/j.scs.2020.102201
Wu, X., Lu, Y., Lin, Y., & Yang, Y. (2019). Measuring the destination accessibility of cycling transfer trips in metro station areas: A big data approach. International Journal of Environmental Research and Public Health, 16(15), 2641. https://doi.org/10.3390/ijerph16152641
Yao, M., Fu, Q., Gao, L., & Li, Z. (2011). Exploring influencing factors to the riding comfort of bicyclists on physically separated bicycle roadways in China using proportional odds model. ICCTP 2011: Towards Sustainable Transportation Systems, 718–727. https://doi.org/10.1061/41186(421)70
Zhang, Y., Chen, X. J., Gao, S., Gong, Y., & Liu, Y. (2024). Integrating smart card records and dockless bike-sharing data to understand the effect of the built environment on cycling as a feeder mode for metro trips. Journal of Transport Geography, 121, 103995. https://doi.org/10.1016/j.jtrangeo.2024.103995
Zhao, P., & Li, S. (2017). Bicycle-metro integration in a growing city: The determinants of cycling as a transfer mode in metro station areas in Beijing. Transportation Research Part A: Policy and Practice, 99, 46–60. https://doi.org/10.1016/j.tra.2017.03.003
Zuo, T., Wei, H., Chen, N., Zhang, C. (2020). First-and-last mile solution via bicycling to improving transit accessibility and advancing transportation equity. Cities, 99, 102614. https://doi.org/10.1016/j.cities.2020.102614

