The analysis of infrastructure subjected to consecutive dynamic loads, such as roads under traffic conditions, presents significant engineering challenges. Traditional simulation techniques, like the Finite Element Method (FEM), become increasingly complex and resource-intensive when modeling these moving loads due to the need for detailed meshing throughout the domain. In the context of digital twin applications for road systems, an efficient alternative is the Arbitrary Lagrangian Eulerian (ALE) approach, which simplifies the problem by treating the moving load as stationary relative to the reference frame, while allowing the infrastructure material to move through a fixed mesh. Long-term predictions of infrastructure performance, such as road damage under repetitive traffic loads, also pose a challenge. Simulating these loads in real-time over extended periods becomes impractical for digital twin road models. To address this, we employ time homogenization techniques alongside the ALE formulation. This approach efficiently models both short-term loading effects, such as vehicle tire impact, and long-term environmental factors like day-night cycles and seasonal temperature variations, helping to predict road deterioration and maintenance needs over years. By integrating these advanced simulation techniques into digital twin road projects, infrastructure management can be optimized for predictive maintenance and improved lifecycle performance.