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Estimating pedestrian demand for active transport evaluation and planning

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  • Dhanani, Ashley
  • Tarkhanyan, Lusine
  • Vaughan, Laura

Abstract

This article presents a recently developed walkability-based approach to evaluating the built environment’s relationship to pedestrian activity, as well as the application of this evaluation in generating a model of pedestrian demand across London derived from built environment indicators. The approach is novel in its integration of space syntax measures to evaluate network accessibility and the use of volume area ratios to measure land use intensity. It utilises high-resolution geographic data surfaces for the generation of the built environment variables. The advantage of using this method is that it allows greater analytical flexibility in transport policy and practice, where the ability to compare the analytical results to other social and spatial indicators is vital for decision-making. Pedestrian density data covering the whole of Greater London are used to test the performance of the variables. The best performing variables are then analysed to determine their weighting in a model of pedestrian demand for London based on the selected built environment indicators. Randomised testing shows that the model is capable of reliably predicting pedestrian demand. It can be used to estimate pedestrian demand both currently and for future scenarios by quantify future changes to the built environment, and thus enabling walking to be quantitatively assessed in the same way as motorised modes. The model can be applied to active travel infrastructure planning and policy evaluation, from the scale of the street or intersection, to larger administrative units. The model also has wider theoretical and policy implications that relate to the spatial structuring of London.

Suggested Citation

  • Dhanani, Ashley & Tarkhanyan, Lusine & Vaughan, Laura, 2017. "Estimating pedestrian demand for active transport evaluation and planning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 54-69.
  • Handle: RePEc:eee:transa:v:103:y:2017:i:c:p:54-69
    DOI: 10.1016/j.tra.2017.05.020
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    Cited by:

    1. Kayvan Karimi, 2018. "Space syntax: consolidation and transformation of an urban research field," Journal of Urban Design, Taylor & Francis Journals, vol. 23(1), pages 1-4, January.
    2. Seungkyu Ryu & Anthony Chen & Jacqueline Su & Xintao Liu & Jiangbo (Gabe) Yu, 2021. "Considering Space Syntax in Bicycle Traffic Assignment with One or More User Classes," Sustainability, MDPI, vol. 13(19), pages 1-15, October.
    3. Tianyang Ge & Wenjun Hou & Yang Xiao, 2023. "Study on the Regeneration of City Centre Spatial Structure Pedestrianisation Based on Space Syntax: Case Study on 21 City Centres in the UK," Land, MDPI, vol. 12(6), pages 1-26, June.
    4. Jing Huang & Xiao Hu & Jieqiong Wang & Andong Lu, 2023. "How Diversity and Accessibility Affect Street Vitality in Historic Districts?," Land, MDPI, vol. 12(1), pages 1-23, January.
    5. Chiara Garau & Alfonso Annunziata & Claudia Yamu, 2020. "The Multi-Method Tool ‘PAST’ for Evaluating Cultural Routes in Historical Cities: Evidence from Cagliari, Italy," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
    6. Yung-Chia Hsueh & Rachel Batchelor & Margaux Liebmann & Ashley Dhanani & Laura Vaughan & Anne-Kathrin Fett & Farhana Mann & Alexandra Pitman, 2022. "A Systematic Review of Studies Describing the Effectiveness, Acceptability, and Potential Harms of Place-Based Interventions to Address Loneliness and Mental Health Problems," IJERPH, MDPI, vol. 19(8), pages 1-29, April.
    7. Adriana Ortegon-Sanchez & Rosemary R. C. McEachan & Alexandra Albert & Chris Cartwright & Nicola Christie & Ashley Dhanani & Shahid Islam & Marcella Ucci & Laura Vaughan, 2021. "Measuring the Built Environment in Studies of Child Health—A Meta-Narrative Review of Associations," IJERPH, MDPI, vol. 18(20), pages 1-34, October.
    8. Antonio A. Barreda-Luna & Juvenal Rodríguez-Reséndiz & Alejandro Flores Rangel & Omar Rodríguez-Abreo, 2022. "Neural Network and Spatial Model to Estimate Sustainable Transport Demand in an Extensive Metropolitan Area," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    9. Kimon Krenz & Ashley Dhanani & Rosemary R. C. McEachan & Kuldeep Sohal & John Wright & Laura Vaughan, 2023. "Linking the Urban Environment and Health: An Innovative Methodology for Measuring Individual-Level Environmental Exposures," IJERPH, MDPI, vol. 20(3), pages 1-22, January.
    10. Choobchian, Pooria & Mohammadi, Ali & Zou, Bo & Hair, Joseph F. & Valinejad, Mahsa & Shin, Jaeyong & Sriraj, P.S., 2024. "Calibrating walkability indicators for commute walk trips: A structural equation modeling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    11. Pajares, Elias & Büttner, Benjamin & Jehle, Ulrike & Nichols, Aaron & Wulfhorst, Gebhard, 2021. "Accessibility by proximity: Addressing the lack of interactive accessibility instruments for active mobility," Journal of Transport Geography, Elsevier, vol. 93(C).
    12. Otsuka, Noriko & Wittowsky, Dirk & Damerau, Marlene & Gerten, Christian, 2021. "Walkability assessment for urban areas around railway stations along the Rhine-Alpine Corridor," Journal of Transport Geography, Elsevier, vol. 93(C).

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