Abstract

A growing number of megacities have been experiencing changes to their landscape due to rapid urbanization trajectories and travel behavior dynamics. Therefore, it is of great significance to investigate the distribution and evolution of a city's urban functional areas over different periods of time. Although the smart card automated fare collection system is already widely used, few studies have used smart card data to infer information about changes in urban functional areas, particularly in developing countries. Thus, this research aims to delineate the dynamic changes that have occurred in urban functional areas based on passengers' travel patterns, using Beijing as a case study. We established a Bayesian framework and applied a Gaussian mixture model derived from transit smart card data in order to gain insight into passengers' travel patterns at station level and then identify the dynamic changes in their corresponding urban functional areas. Our results show that Beijing can be clustered into five different functional areas based on the analysis of corresponding transit station functions: multimodal interchange hub and leisure area; residential area; employment area; mixed but mainly residential area; and mixed residential and employment area. In addition, we found that urban functional areas have experienced slight changes between 2014 and 2017. The findings can be used to inform urban planning strategies designed to tackle urban spatial structure issues, as well as guiding future policy evaluation of urban landscape pattern use.

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Acknowledgments

The authors would like to extend their appreciation to the chief editor and the anonymous reviewers for their valuable comments on the initial draft of this paper. This research is funded by the National Natural Science Foundation of China (Project No. 51808392), the EPSRC (EPSRC Reference: EP/R035148/1), the SCUE Research Fund, and School Funding from the University of Westminster.

References

Alsger, A., A. Tavassoli, M. Mesbah, L. Ferreira, and M. Hickman. 2018. “Public transport trip purpose inference using smart card fare data.” Transp. Res. Part C Emerging Technol. 87: 123–137. https://doi.org/10.1016/j.trc.2017.12.016.
Blainey, S., and C. Mulley. 2013. “Using geographically weighted regression to forecast rail demand in the Sydney Region.” In Australasian Transport Research Forum, 1–16. Queensland, Australia: Australasian Transport Research Forum (ATRF). https://trid.trb.org/view/1285369.
Briand, A. S., E. Côme, M. Trépanier, and L. Oukhellou. 2017. “Analyzing year-to-year changes in public transport passenger behaviour using smart card data.” Transp. Res. Part C Emerging Technol. 79: 274–289. https://doi.org/10.1016/j.trc.2017.03.021.
Cao, M. 2019. “Exploring the relation between transport and social equity: empirical evidence from London and Beijing.” Ph.D. thesis, The Bartlett School of Planning, UCL.
Cao, M., C.-L. Chen, and R. Hickman. 2017. “Transport emissions in Beijing: A scenario planning approach.” Proc. Inst. Civ. Eng. Transp. 170 (2): 65–75. https://doi.org/10.1680/jtran.15.00093.
Cao, M., and R. Hickman. 2018. “Car dependence and housing affordability: An emerging social deprivation issue in London.” Urban Stud. 55 (10): 2088–2105. https://doi.org/10.1177/0042098017712682.
Cao, M., and R. Hickman. 2019. “Understanding travel and differential capabilities and functionings in Beijing.” Transp. Policy 83: 46–56. https://doi.org/10.1016/j.tranpol.2019.08.006.
Cao, M., and R. Hickman. 2020. “Transport, social equity and capabilities in East Beijing.” In Handbook on transport and urban transformation in China, edited by C.-L. Chen, H. Pan, Q. Shen, and J. Wang, 317–333. Cheltenham, UK: Edward Elgar.
Chen, C., J. Chen, and J. Barry. 2009. “Diurnal pattern of transit ridership: A case study of the New York City subway system.” J. Transp. Geogr. 17 (3): 176–186. https://doi.org/10.1016/j.jtrangeo.2008.09.002.
Chen, Y., X. Liu, X. Li, X. Liu, Y. Yao, G. Hu, X. Xu, and F. Pei. 2017. “Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method.” Landscape Urban Plann. 160: 48–60. https://doi.org/10.1016/j.landurbplan.2016.12.001.
Davies, D. L., and D. W. Bouldin. 1979. “A cluster separation measure.” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1 (2): 224–227. https://doi.org/10.1109/TPAMI.1979.4766909.
Dewita, Y., M. Burke, and B. T. H. Yen. 2020. “The relationship between transport, housing and urban form: Affordability of transport and housing in Indonesia.” Case Stud. Transp. Policy 8 (1): 252–262. https://doi.org/10.1016/j.cstp.2019.01.004.
Dewita, Y., B. T. H. Yen, and M. Burke. 2018. “The effect of transport cost on housing affordability: Experiences from the Bandung Metropolitan Area, Indonesia.” Land Use Policy 79: 507–519. https://doi.org/10.1016/j.landusepol.2018.08.043.
Gan, Z., M. Yang, T. Feng, and H. Timmermans. 2020. “Understanding urban mobility patterns from a spatiotemporal perspective: Daily ridership profiles of metro stations.” Transportation 47 (1): 315–336. https://doi.org/10.1007/s11116-018-9885-4.
Geng, W., and G. Yang. 2017. “Partial correlation between spatial and temporal regularities of human mobility.” Sci. Rep. 7 (1): 6249. https://doi.org/10.1038/s41598-017-06508-1.
Gong, Y., Y. Lin, and Z. Duan. 2017. “Exploring the spatiotemporal structure of dynamic urban space using metro smart card records.” Comput. Environ. Urban Syst. 64: 169–183. https://doi.org/10.1016/j.compenvurbsys.2017.02.003.
Goulet-Langlois, G., H. N. Koutsopoulos, and J. Zhao. 2016. “Inferring patterns in the multi-week activity sequences of public transport users.” Transp. Res. Part C Emerging Technol. 64: 1–16. https://doi.org/10.1016/j.trc.2015.12.012.
Halvorsen, A., H. N. Koutsopoulos, S. Lau, T. Au, and J. Zhao. 2016. “Reducing subway crowding: Analysis of an off-peak discount experiment in Hong Kong.” Transp. Res. Rec. 2544 (1): 38–46. https://doi.org/10.3141/2544-05.
Hasan, S., C. M. Schneider, S. V. Ukkusuri, and M. C. González. 2013. “Spatiotemporal patterns of urban human mobility.” J. Stat. Phys. 151 (1–2): 304–318. https://doi.org/10.1007/s10955-012-0645-0.
Hasnat, M. M., and S. Hasan. 2018. “Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data.” Transp. Res. Part C Emerging Technol. 96: 38–54. https://doi.org/10.1016/j.trc.2018.09.006.
Heiden, U., W. Heldens, S. Roessner, K. Segl, T. Esch, and A. Mueller. 2012. “Urban structure type characterization using hyperspectral remote sensing and height information.” Landscape Urban Plann. 105 (4): 361–375. https://doi.org/10.1016/j.landurbplan.2012.01.001.
Huang, J., D. Levinson, J. Wang, and H. Jin. 2019. “Job-worker spatial dynamics in Beijing: Insights from Smart Card Data.” Cities 86: 83–93. https://doi.org/10.1016/j.cities.2018.11.021.
Huang, J., D. Levinson, J. Wang, J. Zhou, and Z. J. Wang. 2018. “Tracking job and housing dynamics with smartcard data.” Proc. Natl. Acad. Sci. 115 (50): 12710–12715. https://doi.org/10.1073/pnas.1815928115.
Jiang, H., and D. Levinson. 2017. “Accessibility and the evaluation of investments on the Beijing subway.” J. Transp. Land Use 10 (1): 395–408.
Kieu, L. M., A. Bhaskar, and E. Chung. 2015. “A modified density-based scanning algorithm with noise for spatial travel pattern analysis from smart card AFC data.” Transp. Res. Part C Emerging Technol. 58: 193–207. https://doi.org/10.1016/j.trc.2015.03.033.
Lee, S. G., and M. Hickman. 2014. “Trip purpose inference using automated fare collection data.” Public Transp. 6 (1–2): 1–20. https://doi.org/10.1007/s12469-013-0077-5.
Li, Y., X. Wang, S. Sun, X. Ma, and G. Lu. 2017. “Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks.” Transp. Res. Part C Emerging Technol. 77: 306–328. https://doi.org/10.1016/j.trc.2017.02.005.
Long, Y., and J. C. Thill. 2015. “Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing.” Comput. Environ. Urban Syst. 53: 19–35. https://doi.org/10.1016/j.compenvurbsys.2015.02.005.
Ma, X., C. Liu, H. Wen, Y. Wang, and Y. J. Wu. 2017. “Understanding commuting patterns using transit smart card data.” J. Transp. Geogr. 58: 135–145. https://doi.org/10.1016/j.jtrangeo.2016.12.001.
Ma, X., J. Zhang, C. Ding, and Y. Wang. 2018. “A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership.” Comput. Environ. Urban Syst. 70: 113–124. https://doi.org/10.1016/j.compenvurbsys.2018.03.001.
Mohamed, K., E. Côme, L. Oukhellou, and M. Verleysen. 2017. “Clustering smart card data for urban mobility analysis.” IEEE Trans. Intell. Transp. Syst. 18 (3): 712–728. https://doi.org/10.1109/TITS.2016.2600515.
Pelletier, M. P., M. Trépanier, and C. Morency. 2011. “Smart card data use in public transit: A literature review.” Transp. Res. Part C Emerging Technol. 19 (4): 557–568. https://doi.org/10.1016/j.trc.2010.12.003.
Pham, H. M., Y. Yamaguchi, and T. Q. Bui. 2011. “A case study on the relation between city planning and urban growth using remote sensing and spatial metrics.” Landscape Urban Plann. 100 (3): 223–230. https://doi.org/10.1016/j.landurbplan.2010.12.009.
Reynolds, D. A., T. F. Quatieri, and R. B. Dunn. 2000. “Speaker verification using adapted Gaussian mixture models.” Digital Signal Process. 10 (1–3): 19–41. https://doi.org/10.1006/dspr.1999.0361.
Rousseeuw, P. J. 1987. “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis.” J. Comput. Appl. Math. 20: 53–65. https://doi.org/10.1016/0377-0427%2887%2990125-7.
Sagl, G., E. Delmelle, and E. Delmelle. 2014. “Mapping collective human activity in an urban environment based on mobile phone data.” Cartogr. Geographic Inf. Sci. 41 (3): 272–285. https://doi.org/10.1080/15230406.2014.888958.
Taylor, B. D., D. Miller, H. Iseki, and C. Fink. 2009. “Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas.” Transp. Res. Part A Policy Pract. 43 (1): 60–77. https://doi.org/10.1016/j.tra.2008.06.007.
Thompson, G. L., and J. R. Brown. 2006. “Explaining variation in transit ridership in U.S. metropolitan areas between 1990 and 2000: Multivariate analysis.” Transp. Res. Rec. 1986 (1): 172–181. https://doi.org/10.1177/0361198106198600121.
Van de Voorde, T., W. Jacquet, and F. Canters. 2011. “Mapping form and function in urban areas: An approach based on urban metrics and continuous impervious surface data.” Landscape Urban Plann. 102 (3): 143–155. https://doi.org/10.1016/j.landurbplan.2011.03.017.
Wang, Z., F. Chen, and T. Fujiyama. 2015. “Carbon emission from urban passenger transportation in Beijing.” Transp. Res. Part D Transp. Environ. 41: 217–227. https://doi.org/10.1016/j.trd.2015.10.001.
Wang, Z. J., F. Chen, B. Wang, and J. L. Huang. 2018. “Passengers’ response to transit fare change: An ex post appraisal using smart card data.” Transportation 45 (5): 1559–1578. https://doi.org/10.1007/s11116-017-9775-1.
Zhang, M., S. He, and P. Zhao. 2018. “Revisiting inequalities in the commuting burden: Institutional constraints and job-housing relationships in Beijing.” J. Transp. Geogr. 71: 58–71. https://doi.org/10.1016/j.jtrangeo.2018.06.024.
Zhang, Y., S. Marshall, and E. Manley. 2019. “Network criticality and the node-place-design model: Classifying metro station areas in Greater London.” J. Transp. Geogr. 79: 102485. https://doi.org/10.1016/j.jtrangeo.2019.102485.
Zhao, J., Q. Qu, F. Zhang, C. Xu, and S. Liu. 2017. “Spatio-temporal analysis of passenger travel patterns in massive smart card data.” IEEE Trans. Intell. Transp. Syst. 18 (11): 3135–3146. https://doi.org/10.1109/TITS.2017.2679179.
Zhao, P., and Y. Cao. 2020. “Commuting inequity and its determinants in Shanghai: New findings from big-data analytics.” Transp. Policy 92: 20–37. https://doi.org/10.1016/j.tranpol.2020.03.006.
Zhao, P., and H. Hu. 2019. “Geographical patterns of traffic congestion in growing megacities: Big data analytics from Beijing.” Cities 92: 164–174. https://doi.org/10.1016/j.cities.2019.03.022.
Zhao, P., H. Yang, L. Kong, Y. Liu, and D. Liu. 2018. “Disintegration of metro and land development in transition China: A dynamic analysis in Beijing.” Transp. Res. Part A Policy Pract. 116: 290–307. https://doi.org/10.1016/j.tra.2018.06.017.
Zhong, C., M. Batty, E. Manley, J. Wang, Z. Wang, F. Chen, and G. Schmitt. 2016. “Variability in regularity: Mining temporal mobility patterns in London, Singapore and Beijing using smart-card data.” PLoS One 11 (2): e0149222.
Zhong, C., X. Huang, S. M. Arisona, G. Schmitt, and M. Batty. 2014. “Inferring building functions from a probabilistic model using public transportation data.” Comput. Environ. Urban Syst. 48: 124–137. https://doi.org/10.1016/j.compenvurbsys.2014.07.004.
Zhu, Y., F. Chen, M. Li, and Z. Wang. 2018. “Inferring the economic attributes of urban rail transit passengers based on individual mobility using multisource data.” Sustainability 10 (11): 4178. https://doi.org/10.3390/su10114178.
Zhu, Y., F. Chen, Z. Wang, and J. Deng. 2019. “Spatio-temporal analysis of rail station ridership determinants in the built environment.” Transportation 46 (6): 2269–2289. https://doi.org/10.1007/s11116-018-9928-x.
Zivkovic, Z. 2004. “Improved adaptive Gaussian mixture model for background subtraction.” In Vol. 2 of Proc., 17th Int. Conf. on Pattern Recognition, 28–31. New York: IEEE.
Zou, Q., X. Yao, P. Zhao, H. Wei, and H. Ren. 2018. “Detecting home location and trip purposes for cardholders by mining smart card transaction data in Beijing subway.” Transportation 45 (3): 919–944. https://doi.org/10.1007/s11116-016-9756-9.

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Go to Journal of Urban Planning and Development
Journal of Urban Planning and Development
Volume 147Issue 2June 2021

History

Received: Dec 22, 2019
Accepted: Oct 16, 2020
Published online: Jan 20, 2021
Published in print: Jun 1, 2021
Discussion open until: Jun 20, 2021

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Associate Professor, Dept. of Highway and Railway Engineering, School of Civil and Architectual Engineering, Beijing Jiaotong Univ., No. 3 Shangyuan Village, Haidian District, Beijing 100089, PR China. Email: [email protected]
Engineer, Transportation Research Centre, Beijing Urban Construction Design and Development Group Co., Limited, No. 5, Fuchengmen Beidajie, Xicheng District, Beijing 100032, PR China. ORCID: https://orcid.org/0000-0003-4574-4401. Email: [email protected]
Research Associate, Dept. of Highway and Railway Engineering, School of Civil and Architectual Engineering, Beijing Jiaotong Univ., No. 3 Shangyuan Village, Haidian District, Beijing 100089, PR China. ORCID: https://orcid.org/0000-0003-4906-5916. Email: [email protected]
Yuerong Zhang [email protected]
Ph.D. Candidate, Bartlett School of Planning, Univ. College London, Central House, 14 Upper Woburn Place, London WC1H 0NN, UK; Teaching Assistant, Bartlett Centre for Advanced Spatial Analysis, Univ. College London, 90 Tottenham Court Rd., London W1T 4TJ, UK. Email: [email protected]
Anahid Basiri [email protected]
Professor, School of Geographical and Earth Sciences, Univ. of Glasgow, Glasgow G12 8QQ, UK. Email: [email protected]
Benjamin Büttner [email protected]
Head of Research Group Accessibility Planning, Dept. of Civil, Geo and Environmental Engineering, Technical Univ. of Munich, Arcisstr. 21, Munich 80333, Germany. Email: [email protected]
Ph.D. Candidate, Bartlett School of Planning, Univ. College London, Central House, 14 Upper Woburn Place, London WC1H 0NN, UK. Email: [email protected]
Senior Lecturer, School of Architecture and Cities, Univ. of Westminster, 35 Marylebone Rd., London NW1 5LS, UK (corresponding author). ORCID: https://orcid.org/0000-0001-8670-4735. Email: [email protected]

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