Smart City Design
Overview
Plan-making systems for UK cities are not currently fit for purpose. Local plans, the major instrument of the statutory planning system, must be modernised to exploit collective data and machine intelligence. This Theme develops these dimensions of smart cities and smart plans and links them to applications in practice.
Case study
Meeting the challenges associated with smart planning for smart cities in a way that delivers practical tools and applications requires integrating and exploiting multiple streams of city data provided by:
- local and national government
- urban analytics and infrastructure firms
- national agencies
- survey data
- human mobility patterns derived from digital traces or social media
These data are used to drive new AI for two purposes:
- automating real-time intelligence for the smart city
- informing longer-term smart city planning to meet the challenges of climate, ageing, housing affordability, and health
Achieving smarter cities that optimise behaviours in the short and long term requires AI that extends and improves on existing models of urban structure, dealing with highly fluid situations dominated by rapid change. This is a major challenge not only for the way that we design cities but also for how AI must deal with many/most human problem-solving contexts.
Supervised and unsupervised learning methods are used to reveal new patterns in large messy mobility datasets such as mobile phone traces, cross-validated with rich survey data to produce spatially, temporally and attribute rich insights into the seismic shift in post-COVID mobility patterns.
Predictive tools for the design of new patterns of transport and land development at different scales are founded on models that take multiple land suitability and mobility indices as inputs. Deriving meaningful interpretations of these models and enabling decision-makers to explore how optimal plans play out over time and space in the context of synthetic AI agent models delivers the explanatory accounts that are essential for public accountability in the use of these methods for city decision making.