A Taxonomy of Digital Twin Applications for Road Infrastructure Safety: A Human-Factors Perspective

Abstract

Road transport systems are vital socio-technical infrastructures but still account for a large share of fatal and serious injuries worldwide. While vehicle technology and traffic management have improved safety on major roads, urban and secondary roads remain challenging owing to varied infrastructure conditions, mixed traffic, and reliance on human judgment. Improving safety in these areas requires approaches that focus on the interactions between humans, systems, and infrastructure. Digital twins (DTs), recognised as dynamic digital models of physical assets, processes, and conditions, are increasingly used for predictive modelling, simulation, and human-centred decision support in transportation. By integrating diverse data, representing evolving risks, and simulating behavioural responses, DTs offer new pathways to reduce human error, aid decision-making, and improve road asset and safety management. However, the variety of DT architectures, functions, and data capabilities creates challenges for their systematic adoption, evaluation, and human-centred design. This study introduces a taxonomy of digital twin applications for urban and rural road asset and safety management from a human factors perspective, developed within the CAMBER project. The taxonomy organizes DT systems according to four dimensions: functional scope, application scale, architectural layers, and integration level. These dimensions describe how DTs support asset and safety management, assess how road conditions affect advanced driver-assistance systems, scale from individual components to system and process levels, and mediate interactions among human decision-makers, infrastructure, and digital services. Developed through a qualitative, problem-driven design, the taxonomy is based on a structured review of recent literature, analysis of CAMBER pilot requirements and evaluation framework. Through examples and discussion of adoption challenges, this study demonstrates how the taxonomy can guide human-centred design, comparison, and scaling of DT-enabled safety applications on urban and secondary roads.

Keywords: Digital Twins, Road Infrastructure Safety, Human Factors, Transportation Systems, Decision Support

DOI: 10.54941/ahfe1007882