The effective operation and maintenance of footway assets is critical for ensuring pedestrian safety, mobility, and the sustainability of urban infrastructure. Traditional maintenance practices, characterized by periodic inspections and reactive repairs, are often inefficient and costly, resulting in high costs, heightened safety risks and suboptimal resource allocation. This paper aims to develop a conceptual framework for the application of digital twin (DT) technology in the management of footway assets, addressing these inefficiencies. Digital Twins (DTs) leverage real-time data and predictive analytics to offer a transformative approach to infrastructure management. The proposed DT-based framework integrates continuous monitoring, predictive maintenance, and standardized condition assessments. By employing IoT sensors and advanced data analytics, the framework enables real-time detection of footway conditions, predicting potential issues before they manifest physically. This predictive capability is expected to reduce maintenance costs by up to 40% and improve safety by decreasing the incidence of accidents by 20%.