Levelling up in AI-powered Smart City

Algorithmic modelling has taken on a pivotal role within the realm of mobility modelling, playing a crucial part in informing decisions made by public systems within Smart Cities. These decisions span a spectrum from traffic management and public safety to waste management. According to a comprehensive analysis by Grand View Research, the global smart cities market attained a valuation of USD 83.9 billion in 2019, with projections indicating a robust compound annual growth rate of 24.7% from 2020 to 2027. Nonetheless, it is imperative to acknowledge the potential unintended consequences of employing algorithmic modelling and AI tools. These tools, while highly effective, have the capacity to inadvertently exacerbate disparities in socioeconomic conditions. As an illustrative example, data-driven mobility models could inadvertently reveal reduced travel patterns in economically disadvantaged areas. While this might appear to optimise overall system efficiency through the reduction of transportation services in these areas, it has the potential to limit access to critical opportunities, thereby aggravating pre-existing inequalities. Given these complex considerations, our focal objective encompasses an exhaustive exploration at the confluence of human mobility data, urban transportation data, and socioeconomic urban data. The core tenets of this inquiry encompass the following pivotal dimensions:

• Thoroughly scrutinizing the ramifications of algorithmic data-driven modelling on the analysis of mobility, and the consequential impacts on the decisions made by public systems. This encompasses a diverse array of domains such as traffic prognostication, urban layout, and public safety.
• Delving into the latent potential of AI tools to magnify and accentuate prevailing socioeconomic disparities within local communities.
• Crafting machine learning strategies that seamlessly integrate notions of equity into data-driven choices, in alignment with the overarching objectives laid out in the UK Levelling-Up White Paper. This endeavour places a primary emphasis on the spheres of urban planning and transportation.
• Devising a comprehensive set of guidelines tailored for policymakers and practitioners, aimed at upholding principles of fairness and parity in the decision-making processes pertaining to urban and transportation planning.

In summary, this undertaking seeks to navigate the intricate interplay of algorithmic modelling, urban dynamics, and socioeconomic realities. Its outcome is intended to serve as a guiding light for equitable and judicious decision-making in the realms of urban and transportation planning.