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Spatial-interaction network analysis of built environmental influence on daily public transport demand

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  • Liu, Xintao
  • Wu, Jiawei
  • Huang, Jianwei
  • Zhang, Junwei
  • Chen, Bi Yu
  • Chen, Anthony

Abstract

Many studies have evaluated the influence of the built environment on public transport. Some studies assign subjective weights to environmental factors, which could oversimplify spatial heterogeneity and overlook the temporal dimension. On the other hand, the spatial-interaction network of public transport system is seldom considered. In this paper, we propose an improved framework to explore how individual factors unevenly affect public transport demand over space and time using a geographically and temporally weighted regression (GTWR) model. The proposed framework extends the local built environmental factors by including two network factors extracted from the spatial-interaction network of the public transport system. We conduct a case study in Beijing, China using 686 traffic analysis zones (TAZs). The actual usage of public transport, namely the public transport index (PTI), is estimated by passenger flow divided by the total amount of human flow in a given TAZ. The daily patterns of the spatial heterogeneity in some selected places in the study area is identified and analyzed. It is also found that the estimated coefficient of the variables of the spatial-interaction network is significantly larger than other static environmental factors, indicating that spatial-interaction network can more effectively reflect spatiotemporal heterogeneity in public transport demand. This study provides a better decision-making support for more accurately identifying which factors are most worthy of development, and when and where they can be implemented to improve public transit services.

Suggested Citation

  • Liu, Xintao & Wu, Jiawei & Huang, Jianwei & Zhang, Junwei & Chen, Bi Yu & Chen, Anthony, 2021. "Spatial-interaction network analysis of built environmental influence on daily public transport demand," Journal of Transport Geography, Elsevier, vol. 92(C).
    • Handle: RePEc:eee:jotrge:v:92:y:2021:i:c:s0966692321000442
    DOI: 10.1016/j.jtrangeo.2021.102991
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    References listed on IDEAS

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    2. Chen, Wendong & Chen, Xuewu & Cheng, Long & Liu, Xize & Chen, Jingxu, 2022. "Delineating borders of urban activity zones with free-floating bike sharing spatial interaction network," Journal of Transport Geography, Elsevier, vol. 104(C).
    3. Gimenez-Nadal, José Ignacio & Gracia-Lazaro, Carlos & Molina, José Alberto, 2021. "Bike-Sharing: Network Efficiency and Demand Profiles," IZA Discussion Papers 14818, Institute of Labor Economics (IZA).
    4. Wang, Zhiqi & Zhang, Yufeng & Jia, Bin & Gao, Ziyou, 2024. "Comparative Analysis of Usage Patterns and Underlying Determinants for Ride-hailing and Traditional Taxi Services: A Chicago Case Study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).

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