Identifying the Spatiotemporal Metro-to-Bus Transfer Deserts in Shanghai, China
Publication: Journal of Urban Planning and Development
Volume 149, Issue 2
Abstract
In the majority of metropolitan areas in China, the ground-level bus is the dominating feeder mode for metro systems. For enhancing the intermodal integration, it is necessary to evaluate the transfer accessibility between metro and bus considering the spatiotemporal dimensions with finer resolution. This study developed a methodology to identify the metro-to-bus transfer desert by considering the spatiotemporal dynamics on both service supply and passenger demand based on smart card data and automatic vehicle location data. In order to provide a more accurate and detailed depiction of results and stronger policy implications, this research aggregates to a higher spatial resolution (i.e., metro stops), instead of utilizing census tracts or administrative zones used in available research. The proposed methodology was implemented in the case study of the metropolitan area of Shanghai. Our empirical analysis highlights transfer service deficiency existing across the metropolitan area of Shanghai throughout the day, and varies both on spatially and temporally. Peak hours are characterized by better transfer accessibility levels on average but worse performances on metro stops with high transfer volume compared with off-peak hours. Because the developed methodology makes use of widely available input data, policymakers and transit agencies could replicate such gap analysis of local transit services in other contexts around the world.
Get full access to this article
View all available purchase options and get full access to this article.
References
Aman, J. J. C., and J. Smith-Colin. 2020. “Transit deserts: Equity analysis of public transit accessibility.” J. Transp. Geogr. 89: 102869. https://doi.org/10.1016/j.jtrangeo.2020.102869.
Baker, J., D. A. Swanson, J. Tayman, and L. M. Tedrow. 2017. “Sources of demographic information.” In Cohort change ratios and their applications, 35–44. Cham, Switzerland: Springer.
Bejleri, I., S. Noh, Z. Gu, R. L. Steiner, and S. M. Winter. 2018. “Analytical method to determine transportation service gaps for transportation disadvantaged populations.” Transp. Res. Rec. 2672 (8): 649–661. https://doi.org/10.1177/0361198118794290.
Cai, M., J. Jiao, M. Luo, and Y. Liu. 2020. “Identifying transit deserts for low-income commuters in Wuhan Metropolitan Area, China.” Transp. Res. Part D Transp. Environ. 82 (1): 102292. https://doi.org/10.1016/j.trd.2020.102292.
Chen, B. Y., H. Yuan, Q. Li, D. Wang, S.-L. Shaw, H.-P. Chen, and W. H. Lam. 2017. “Measuring place-based accessibility under travel time uncertainty.” Int. J. Geogr. Inf. Sci. 31 (4): 783–804. https://doi.org/10.1080/13658816.2016.1238919.
Chen, E., W. Zhang, Z. Ye, and M. Yang. 2020. “Unraveling latent transfer patterns between metro and bus from large-scale smart card data.” IEEE Trans. Intell. Transp. Syst. 99: 1–15. https://doi.org/10.1109/TITS.2019.2958673.
Farber, S., B. Ritter, and L. Fu. 2016. “Space–time mismatch between transit service and observed travel patterns in the Wasatch Front, Utah: A social equity perspective.” Travel Behav. Soc. 4: 40–48. https://doi.org/10.1016/j.tbs.2016.01.001.
Fayyaz, S. K., X. C. Liu, and R. J. Porter. 2017. “Dynamic transit accessibility and transit gap causality analysis.” J. Transp. Geogr. 59: 27–39. https://doi.org/10.1016/j.jtrangeo.2017.01.006.
Fransen, K., T. Neutens, S. Farber, P. Maeyer, G. Deruyter, and F. Witlox. 2015. “Identifying public transport gaps using time-dependent accessibility levels.” J. Transp. Geogr. 48: 176–187. https://doi.org/10.1016/j.jtrangeo.2015.09.008.
Hansen, W. G. 1959. “How accessibility shapes land use.” J. Am. Inst. Plann. 25 (1): 73–76. https://doi.org/10.1080/01944365908978307.
Huang, Z., L. Xu, Y. Lin, P. Wu, and B. Feng. 2019. “Citywide metro-to-bus transfer behavior identification based on combined data from Smart Cards and GPS.” Appl. Sci. 9 (17): 3597–3614. https://doi.org/10.3390/app9173597.
Jiao, J. 2017. “Identifying transit deserts in major Texas cities where the supplies missed the demands.” J. Transp. Land Use 10 (1): 529–540. https://doi.org/10.5198/jtlu.2017.899.
Jiao, J., and M. Cai. 2020. “Using open source data to identify transit deserts in four major Chinese cities.” ISPRS Int. J. Geo-Inf. 9 (2): 100. https://doi.org/10.3390/ijgi9020100.
Jiao, J., and M. Dillivan. 2013. “Transit deserts: The gap between demand and supply.” J. Public Transp. 16 (3): 23–39. https://doi.org/10.5038/2375-0901.16.3.2.
Jiao, J., A. V. Moudon, J. Ulmer, P. M. Hurvitz, and A. Drewnowski. 2012. “How to identify food deserts: Measuring physical and economic access to supermarkets in King County Washington.” Am. J. Public Health 102 (10): e32–e39. https://doi.org/10.2105/AJPH.2012.300675.
Jin, H., and Y. Lu. 2021. “SAR-Gi*: Taking a spatial approach to understand food deserts and food swamps.” Appl. Geogr. 134: 102529. https://doi.org/10.1016/j.apgeog.2021.102529.
Kim, D., and J. Park. 2020. “Assessing social and spatial equity of neighborhood retail and service access in Seoul, South Korea.” Sustainability 12 (20): 8537. https://doi.org/10.3390/su12208537.
Kim, H., and F. Wang. 2019. “Disparity in spatial access to public daycare and kindergarten across GIS-constructed regions in Seoul, South Korea.” Sustainability 11 (19): 5503. https://doi.org/10.3390/su11195503.
Lee, H. K., J. Jao, and S. J. Choi. 2021. “Identifying spatiotemporal transit deserts in Seoul, South Korea.” J. Transp. Geogr. 95: 103145. https://doi.org/10.1016/j.jtrangeo.2021.103145.
Lee, J.-H., and H.-J. Kim. 2019. “Identification of spatial distribution of an aged population and analysis on characterization of the cluster: Focusing on Seoul Metropolitan Area.” J. Digital Contents Soc. 20 (7): 1365–1371. https://doi.org/10.9728/dcs.2019.20.7.1365.
Li, Y., and W. “David” Fan. 2020. “Modeling and evaluating public transit equity and accessibility by integrating general transit feed specification data: Case study of the city of Charlotte.” J. Transp. Eng. Part A. Syst. 146 (10): 04020112. https://doi.org/10.1061/JTEPBS.0000426.
Ma, X., Y. Ji, M. Yang, Y. Jin, and X. Tan. 2018. “Understanding bikeshare mode as a feeder to metro by isolating metro-bikeshare transfers from smart card data.” Transp. Policy 71: 57–69. https://doi.org/10.1016/j.tranpol.2018.07.008.
Moya-Gómez, B., M. H. Salas-Olmedo, J. C. García-Palomares, and J. Gutiérrez. 2018. “Dynamic accessibility using big data: The role of the changing conditions of network congestion and destination attractiveness.” Networks Spatial Econ. 18: 273–290. https://doi.org/10.1007/s11067-017-9348-z.
Páez, A., R. G. Mercado, S. Farber, C. Morency, and M. Roorda. 2010. “Relative accessibility deprivation indicators for urban settings: Definitions and application to food deserts in Montreal.” Urban Stud. 47 (7): 1415–1438. https://doi.org/10.1177/0042098009353626.
SICCTD (Shanghai Institute of City Construction and Transportation Development). 2015. “Main results of the fifth comprehensive traffic survey in Shanghai.” Traffic Transp. 31 (6): 15–18.
Tribby, C. P., and P. A. Zandbergen. 2012. “High-resolution spatio-temporal modeling of public transit accessibility.” Appl. Geogr. 34: 345–355. https://doi.org/10.1016/j.apgeog.2011.12.008.
Wang, Y., B. Y. Chen, H. Yuan, D. Wang, W. H. K. Lam, and Q. Li. 2018. “Measuring temporal variation of location-based accessibility using space-time utility perspective.” J. Transp. Geogr. 73: 13–24. https://doi.org/10.1016/j.jtrangeo.2018.10.002.
Widener, M. J., and J. Shannon. 2014. “When are food deserts? Integrating time into research on food accessibility.” Health Place 30: 1–3. https://doi.org/10.1016/j.healthplace.2014.07.011.
Williams, S., A. White, P. Waiganjo, D. Orwa, and J. Klopp. 2015. “The digital Matatu project: Using cell phones to create an open source data for Nairobi’s semi-formal bus system.” J. Transp. Geogr. 49: 39–51. https://doi.org/10.1016/j.jtrangeo.2015.10.005.
Yun, S. B., S. Kim, S. Ju, J. Noh, C. Kim, M. S. Wong, and J. Heo. 2020. “Analysis of accessibility to emergency rooms by dynamic population from mobile phone data: Geography of social inequity in South Korea.” PLoS One 15 (4): e0231079.
Information & Authors
Information
Published In
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 2, 2022
Accepted: Feb 17, 2023
Published online: Apr 13, 2023
Published in print: Jun 1, 2023
Discussion open until: Sep 13, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.