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Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations

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Abstract

Smart card data derived from automatic fare collection (AFC) systems of public transit enable us to study resident movement from a macro perspective. The rhythms of traffic generated by different land uses differ, reflecting differences in human activity patterns. Thus, an understanding of daily ridership and mobility patterns requires an understanding of the relationship between daily ridership patterns and characteristics of stations and their direct environment. Unfortunately, few studies have investigated this relationship. This study aims to propose a framework of identifying urban mobility patterns and urban dynamics from a spatiotemporal perspective and pointing out the linkages between mobility and land cover/land use (LCLU). Relying on 1 month’s transactions data from the AFC system of Nanjing metro, the 110 metro stations are classified into 7 clusters named as employment-oriented stations, residential-oriented stations, spatial mismatched stations, etc., each characterized by a distinct ridership pattern (combining boarding and alighting). A comparison of the peak hourly ridership of the seven clusters is conducted to verify whether the clustering results are reasonable or not. Finally, a multinomial logit model is used to estimate the relationship between characteristics of the local environment and cluster membership. Results show that the classification based on ridership patterns leads to meaningful interpretable clusters and that significant associations exist between local LCLU characteristics, distance to the city center and cluster membership. The analytical framework and findings may be beneficial for improving service efficiency of public transportation and urban planning.

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Notes

  1. We examined the normalized data (see subsection “Variable selection and normalization”) of station boardings and alightings between weekdays, but no significant difference was found.

  2. Two metro stations (Lukou Airport Station—the terminal station of Metro Line S1 and Jinniuhu Station—the terminal station of Metro Line S8) were finally excluded due to two reasons. One is that the ridership patterns in the two stations are different from that of other stations, and they are often classified into a stand-alone cluster when they are considered in the cluster analysis. The other is that the SDV within the two stations is higher than the SDV without the two stations of the same number of clusters. For example, with 7 clusters, the SDV within them is 530.88 while the SDV without them is just 424.90.

  3. Fuzimiao, also refers to Confucius Temple, located in Qinhuai District (near the city center), is a very famous open AAAAA (the highest grade of Chinese scenic spots) scenic spot in Nanjing. With the ancient magnificent architectures, profound culture and superior geographical position, it has been one of the top tourist spots in Nanjing with an average of more than 100 thousand visitors per day.

  4. The distance threshold of the influence area of metro station is identified 800 m (0.5 mile) in the study, which is generally accepted and used as the standard walking distance and the adequate representation of a person’s willingness to walk to rail transit (for example: Kuby et al. 2004; Hess and Almeida 2007; Zhao et al. 2013). Moreover, the Ministry of Housing and Urban–Rural Development of People’s Republic of China (MOHURD) launched a design guidance of urban rail—“Guidelines for Planning and Design of Urban Rail”, and defined a radius of 800 m as the influence area of rail stations (MOHURD 2015).

  5. According to the official information from National Development and Reform Commission of People’s Republic of China, there will be 15 metro lines under operation or building in Nanjing by 2020. http://www.sdpc.gov.cn/zcfb/zcfbtz/201505/t20150520_692548.html.

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Acknowledgements

This research is funded by National Natural Science Foundation of China (Grant Nos. 71771049, 51378120 and 51338003), the Six Talent Peaks Project in Jiangsu Province (Grant No. 2016-JY-003) and China Scholarship Council (Grant No. 201606090149). We are grateful for the valuable suggestions from the associate editor (Professor Mark Horner) and three anonymous reviewers.

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Authors and Affiliations

  1. School of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing, 211189, People’s Republic of China

    Zuoxian Gan & Min Yang

  2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, No. 2 Southeast University Road, Nanjing, 211189, People’s Republic of China

    Zuoxian Gan & Min Yang

  3. Urban Planning Groups, Department of Urban Science and System, Eindhoven University of Technology, PO Box 513, 5600, Eindhoven, The Netherlands

    Tao Feng & Harry Timmermans

  4. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, People’s Republic of China

    Harry Timmermans

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  1. Zuoxian Gan
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  2. Min Yang
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  3. Tao Feng
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ZG Literature Search and Review, Data Analysis and Manuscript Writing. MY Content planning and Manuscript Revising. TF Manuscript Revising and Editing. HT Manuscript Revising and Editing.

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Correspondence to Min Yang.

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Gan, Z., Yang, M., Feng, T. et al. Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations. Transportation 47, 315–336 (2020). https://doi.org/10.1007/s11116-018-9885-4

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