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The impact of street network connectivity on pedestrian volume

Author

Listed:
  • Amir Hajrasouliha

    (University of Utah, USA)

  • Li Yin

    (University at Buffalo, The State University of New York, USA)

Abstract

This paper investigates the impact of street network connectivity on pedestrian volume. Street network connectivity measured in most current studies captures only the metric characteristics of streets or physical connectivity. A whole different type of connectivity, visual connectivity, is largely ignored. Described in basic terms, higher physical connectivity means shorter travel time to reach the same number of destinations while higher visual connectivity means fewer turns to see the same number of destinations. Despite the correlation of these two connectivity constructs, studying both physical and visual connectivity is essential to better understand the role of street network on pedestrian activity. Using pedestrian counts of 302 street segments in Buffalo, New York, structural equation modelling highlights the multiple relationships between street network connectivity, built environment characteristics, and pedestrian volumes. Our findings suggest that both the conventional metric-based measure of physical connectivity and geometric-based measure of visual connectivity have significant positive impacts on pedestrian volumes, together with job density and land use mix. This outcome can encourage practitioners to pay attention to both the geometry of street network and its metric characteristics in order to create a pedestrian-friendly environment.

Suggested Citation

  • Amir Hajrasouliha & Li Yin, 2015. "The impact of street network connectivity on pedestrian volume," Urban Studies, Urban Studies Journal Limited, vol. 52(13), pages 2483-2497, October.
    • Handle: RePEc:sae:urbstu:v:52:y:2015:i:13:p:2483-2497
    DOI: 10.1177/0042098014544763
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    References listed on IDEAS

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    Cited by:

    1. Boeing, Geoff, 2019. "Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood," SocArXiv 7fxjz, Center for Open Science.
    2. Boeing, Geoff, 2017. "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks," SocArXiv q86sd, Center for Open Science.
    3. Shatu, Farjana & Yigitcanlar, Tan & Bunker, Jonathan, 2019. "Shortest path distance vs. least directional change: Empirical testing of space syntax and geographic theories concerning pedestrian route choice behaviour," Journal of Transport Geography, Elsevier, vol. 74(C), pages 37-52.

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