In response to the climate emergency many UK cities and organisations are now investing in Internet of Things sensors and have access to real-time metrics such as traffic flows, air quality, and passenger data to support strategies to reduce their carbon footprints. However, with the pressing need to respond to climate change, the 115 urban centres in the UK may well take individual paths to sensor deployment, data management and data access, potentially compounding complex problems by a lack of standardisation and consistency to their approach.
The UKCRIC Urban Observatories are exemplars of how to collect and manage multiple real-time indicators to capture how a city operates and how policy and infrastructure changes ripple out from their loci to impact other sectors and places. Much of the data that is collected by the Urban Observatories relates to mobility and its environmental impact.
The Urban Observatories have developed a common ethos and data sharing access protocols but the challenge addressed in this research is to understand how these can be scaled to regional and national studies to support agile and data driven responses.
To address this challenge, this collaboration is setting the foundations for a flexible but homogenous approach by cataloguing existing sensor capabilities, benchmarking current metadata standards, developing mechanisms for data standardisation and data onboarding and exploring applications of real-time data for future urban digital twins. The project has developed a living sensor catalogue to complement existing DfT portals and through stakeholder engagement events has highlighted important issues in capturing key metadata for decision makers.
As a result, a number of use cases have been developed that explore how new sensor assets data can then be exploited with improved models. An onboarding prototype has used the Urban Data Exchange model to demonstrate technology agnostic onboarding and common mapping to smart data models to achieve standardisation.
From these a number of use cases including machine learning based traffic prediction, modelling of Low Traffic Neighbourhoods (LTNs) and monitoring black carbon have been prototyped, demonstrating the potential future use cases of digital twins. Ultimately, the project is developing the foundations for city, regional and national twins for local transport and demonstrating the role that real-time high-resolution metrics can play.