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Using multiple hybrid spatial design network analysis to predict longitudinal effect of a major city centre redevelopment on pedestrian flows

Cooper, Crispin, Harvey, Ian, Orford, Scott and Chiaradia, Alain 2019. Using multiple hybrid spatial design network analysis to predict longitudinal effect of a major city centre redevelopment on pedestrian flows. Transportation 10.1007/s11116-019-10072-0

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Abstract

Predicting how changes to the urban environment layout will affect the spatial distribution of pedestrian flows is important for environmental, social and economic sustainability. We present longitudinal evaluation of a model of the effect of urban environmental layout change in a city centre (Cardiff 2007–2010), on pedestrian flows. Our model can be classed as regression based direct demand using Multiple Hybrid Spatial Design Network Analysis (MH-sDNA) assignment, which bridges the gap between direct demand models, facility-based activity estimation and spatial network analysis (which can also be conceived as a pedestrian route assignment based direct demand model). Multiple theoretical flows are computed based on retail floor area: everywhere to shops, shop to shop, railway stations to shops and parking to shops. Route assignment, in contrast to the usual approach of shortest path only, is based on a hybrid of shortest path and least directional change (most direct) with a degree of randomization. The calibration process determines a suitable balance of theoretical flows to best match observed pedestrian flows, using generalized cross-validation to prevent overfit. Validation shows that the model successfully predicts the effect of layout change on flows of up to approx. 8000 pedestrians per hour based on counts spanning a 1 km2 city centre, calibrated on 2007 data and validated to 2010 and 2011. This is the first time, to our knowledge, that a pedestrian flow model with assignment has been evaluated for its ability to forecast the effect of urban layout changes over time.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Data Innovation Research Institute (DIURI)
Geography and Planning (GEOPL)
Sustainable Places Research Institute (PLACES)
Wales Institute of Social & Economic Research, Data & Methods (WISERD)
Publisher: Springer Verlag (Germany)
ISSN: 0049-4488
Date of First Compliant Deposit: 27 November 2019
Date of Acceptance: 22 November 2019
Last Modified: 01 Jan 2020 00:47
URI: http://orca.cf.ac.uk/id/eprint/127219

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