Harnessing digital twin technology in smart transportation system

This study investigates how digital twins (DTs) in transportation systems can be implemented in a smart system toolbox to comply with the United Nations&s; Sustainable Development Goals (SDGs). In this study, we conduct a meta-analysis of 38 published papers on the influence of DT features on transport networks&s; performance and sustainability, including real-time data monitoring, predictive maintenance, and scenario planning. Our study uses a case-oriented methodology, homing in on smart city initiatives that use DT technology in the transportation sector. Data derived for the analysis were based on figures obtained from transport authorities, stakeholders, and literature, which were filtered using inclusion and exclusion criteria to ensure all studies included were relevant and valid. The Hedges&s; method was used to measure effect size and adjusted for fixed and random effects under different scenarios. We have shown that the DT revolution significantly impacts the effectiveness and sustainability of transport systems, the major drivers being predictive maintenance and real-time optimization. The meta-analysis demonstrated more potent effects in studies with larger sample sizes and greater study variability. However, considerable remaining heterogeneity indicates that the amount of DT uptake depends on the region, type of transportation, and technology available. The model fits statistics like Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), representing a stable and sound model. The findings of this study highlight the necessity for building context-specific approaches and the relevance of future work to overcome the barriers of cost, data privacy, and interoperability in scaling up DT technology for sustainable transportation.