A Data-Driven Optimisation Approach to Urban Multi-Site Selection for Public Services and Retails.
VRCAI '19: The 17th International Conference on Virtual-Reality Continuum and its Applications in Industry Brisbane QLD Australia November, 2019(2019)
摘要
Urban lifestyle depends on public services and retails, of which site locations matter to convenience for residents. We introduce a novel approach to the systematic multi-site selection for public services and retails in an urban context. It takes as input a set of data about an urban area and generates an optimal configuration of two-dimensional locations for urban sites on public services and retails. We achieve this goal using data-driven optimisation entangling deep learning. The proposed approach can cost-efficiently generate a multi-site location plan considering representative site selection criteria, including coverage, dispersion and accessibility. It also complies with the local plan and the predicted suitability regarding land-use zoning.
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关键词
multi-site selection, data-driven optimisation, deep learning
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