Exploring city digital twins as policy tools: A task-based approach to generating synthetic data on urban mobility

Abstract

This article discusses the technology of city digital twins (CDTs) and its potential applications in the policymaking context. The article analyzes the history of the development of the concept of digital twins and how it is now being adopted on a city-scale. One of the most advanced projects in the field—Virtual Singapore—is discussed in detail to determine the scope of its potential domains of application and highlight challenges associated with it. Concerns related to data privacy, availability, and its applicability for predictive simulations are analyzed, and potential usage of synthetic data is proposed as a way to address these challenges. The authors argue that despite the abundance of urban data, the historical data are not always applicable for predictions about the events for which there does not exist any data, as well as discuss the potential privacy challenges of the usage of micro-level individual mobility data in CDTs. A task-based approach to urban mobility data generation is proposed in the last section of the article. This approach suggests that city authorities can establish services responsible for asking people to conduct certain activities in an urban environment in order to create data for possible policy interventions for which there does not exist useful historical data. This approach can help in addressing the challenges associated with the availability of data without raising privacy concerns, as the data generated through this approach will not represent any real individual in society.

Synthetic population data for small area estimation in the United States

“…This can facilitate precise estimation and analysis of population patterns, trends, and changes at the local level [Lomax and Smith, 2017; Wu et al, 2022]. Such data can also be used to empower policymakers and planners to simulate and evaluate the impact of various policies, interventions, or scenarios on individual behavior and movement within urban or regional contexts [He et al, 2020; Lin and Xiao, 2023a; Papyshev and Yarime, 2021; Tanton et al, 2009], thus supporting applications in domains such as public health [Grefenstette et al, 2013; Spooner et al, 2021] and transportation planning [Hörl and Balac, 2021, Zhu and Ferreira, 2014]. In addition, there is existing literature on enhancing synthetic population data by incorporating census variables with external data sources such as health and commercial surveys [Spooner et al, 2021; Morrissey et al, 2015].…”

Human mobility data and analysis for urban resilience: A systematic review

“…Another simulation technique to combine with HMDA research is the digital twin city. A digital twin city is a digital representation of a city, enabling comprehensive data exchange and containing models, simulations, and algorithms describing features and behaviors, including mobility, in the real world city (Papyshev and Yarime, 2021; Dembski et al, 2020). How to relate HMDA research with a digital twin city has not yet been explored.…”

Adapting a Digital Twin to Enable Real-Time Water Sensitive Urban Design Decision-Making

“…The origins of Digital Twins (DT), lie in monitoring the performance of real-world entities such as off-shore oil rigs, remote buildings and more recently whole urban environments (Batty, 2018;Chaplin et al, 2020;Grieves, 2019). While urban scale DTs were originally conceived to enable 'now casting' for optimal, smart infrastructure action and management, responsive to the inflow of detailed real-time monitoring data from their physical counterpart, their application is rapidly expanding beyond this use (Papyshev & Yarime, 2021). Recognition of the intersections between the physical and digital infrastructure of DTs, and those of Decision Support Systems (DSS), particularly urban scale DTs and DSS designed to support city scale decisionmaking is growing (Chaplin et al, 2020).…”