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Spatiotemporal exploration of Melbourne pedestrian demand

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  • Pfiester, Laura Mali
  • Thompson, Russell G.
  • Zhang, Lele

Abstract

Creating a cityscape conducive to a safe and efficient pedestrian experience requires a holistic understanding of the relationship between the built structures of a city and the movement of individuals within it. To empower policy makers to design, implement, and successfully deliver measures aimed at reducing footpath congestion and improving pedestrian safety the link between pedestrian volume and different features of the built environment needs to be investigated. Observed pedestrian counts at 50 intersections across the City of Melbourne are used as the input dependent variable of two regression models. A global ordinary least squares regression model and a local geographically weighted regression model are generated and evaluated for best fit of purpose. Spatiotemporal statistical handling is employed to clean the data of contextual anomalies. The output of the regression models identified eight key features as the most statistically significant predictors of pedestrian volume in Melbourne, Australia. These features include distance to schools and train stations and measures of footpath connectivity. This study reveals that due to significant spatial and temporal non-stationarity exhibited between pedestrian count sensors and built environment variables, the geographically weighted regression is the most appropriate modelling technique. This paper presents a methodology for the creation of a robust pedestrian prediction model.

Suggested Citation

  • Pfiester, Laura Mali & Thompson, Russell G. & Zhang, Lele, 2021. "Spatiotemporal exploration of Melbourne pedestrian demand," Journal of Transport Geography, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:jotrge:v:95:y:2021:i:c:s0966692321002040
    DOI: 10.1016/j.jtrangeo.2021.103151
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    References listed on IDEAS

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    1. Yang, Hongtai & Lu, Xiaozhao & Cherry, Christopher & Liu, Xiaohan & Li, Yanlai, 2017. "Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 64(C), pages 184-194.
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    3. Schneider, Robert J. & Arnold, Lindsay S. & Ragland, David R., 2009. "A Pilot Model for Estimating Pedestrian Intersection Crossing Volumes," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3nr8h66j, Institute of Transportation Studies, UC Berkeley.
    4. Marija Bezbradica & Heather Ruskin, 2020. "Understanding Urban Mobility and Pedestrian Movement," Chapters, in: Vito Bobek (ed.), Smart Urban Development, IntechOpen.
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    Cited by:

    1. Weifeng Li & Jiawei He & Qing Yu & Yujiao Chang & Peng Liu, 2021. "Using POI Data to Identify the Demand for Pedestrian Crossing Facilities at Mid-Block," Sustainability, MDPI, vol. 13(23), pages 1-13, November.
    2. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    3. Zaouche, Mounia & Bode, Nikolai W.F., 2023. "Bayesian spatio-temporal models for mapping urban pedestrian traffic," Journal of Transport Geography, Elsevier, vol. 111(C).

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