Walking in the cities without ground, how 3d complex network volumetrics improve analysis

Lingzhu Zhang and Alain JF Chiaradia

Environment and Planning B, 2022, vol. 49, issue 7, 1857-1874

Abstract: Pedestrian route choice, wayfinding behaviour and movement pattern research rely on objective spatial configuration model and analysis. In 3D indoor and outdoor multi-level buildings and urban built environments (IO-ML-BE), spatial configuration analysis allows to quantify and control for route choice and wayfinding complexity/difficulty. Our contribution is to compare the interaction of the level of definition (LOD) of indoor and outdoor multi-level pedestrian network spatial models and complexity metric analyses. Most studies are indoor or outdoor and oversimplify multi-level vertical connections. Using a novel open data set of a large-scale 3D centreline pedestrian network which implement transport geography 2D data model principles in 3D, nine spatial models and twelve spatial complexity analyses of a large-scale 3D IO-ML-BE are empirically tested with observed pedestrian movement patterns ( N = 17,307). Bivariate regression analyses show that the association with movement pattern increases steadily from R 2 ≈ 0.29 to 0.56 (space syntax, 2.5D) and from R 2 ≈ 0.54 to 0.72 (3D sDNA) as the 3D transport geography spatial model LOD and completeness increases. A multivariate stepwise regression analysis tests the bi-variate findings. A novel 3D hybrid angular-Euclidean analysis was tested for the objective description of 3D multi-level IO-ML-BE route choice and wayfinding complexity. The results suggest that pedestrian route choice, wayfinding and movement pattern analysis and prediction research in a multi-level IO-ML-BE should use high-definition 3D transport geography network spatial model and include interdependent outdoor and indoor spaces with detailed vertical transitions.

Keywords: Route choice; 3D sDNA; transport geography; urban volumetrics; pedestrian volume (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:49:y:2022:i:7:p:1857-1874

DOI: 10.1177/23998083211070567

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