Exploring Digital Twins for Urban Mobility:Applications and the Case Study of Helsinki

Abstract

Digital twin technology is increasingly explored as a dynamic, data-driven tool for simulation, monitoring, and optimizing urban transport systems. This paper reviews the current landscape of digital twin applications in urban context, with a focus on their capabilities for predictive analytics, scenario evaluation ass a support for decision making for urban mobility interventions. Based on a review of the latest literature, it examines the conceptual frameworks and applied digital twins' models across different cities. Additionally, this study presents a real-world case study from Helsinki, offering insights into the development and integration of digital twins within a complex urban mobility system. The study contributes to the state of the art by identifying key implementation challenges and proposing future research directions to advance the practical application of digital twin technologies in urban mobility.