Journal
JOURNAL OF URBAN TECHNOLOGY
Volume 29, Issue 2, Pages 99-114Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/10630732.2021.2001713
Keywords
Planning support system (PSS); applied urban model; street network; deep learning; urban design
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The study introduces a novel approach leveraging deep neural networks and planning guidance to automate street network generation, suggesting that incorporating planning knowledge improves the accuracy of model predictions. The new tool allows both professionals and lay users to systematically and intuitively explore benchmark proposals for comparisons and evaluations.
Deep learning applications in shaping ad hoc planning proposals are limited by the difficulty of integrating professional knowledge about cities with artificial intelligence. We propose a novel, complementary use of deep neural networks and planning guidance to automate street network generation that can be context-aware, learning-based, and user-guided. The model tests suggest that the incorporation of planning knowledge (e.g., road junctions and neighborhood types) in the model training leads to a more realistic prediction of street configurations. Furthermore, the new tool provides both professional and lay users an opportunity to systematically and intuitively explore benchmark proposals for comparisons and further evaluations.
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