Data is crucial for both multimodal and multidisciplinary analysis of mobility systems. It provides the foundation for understanding and optimizing interactions between various mobility systems and other engineered and natural systems. By collecting and analyzing data from these interconnected systems, we can gain insights into traffic patterns, system efficiency, and potential vulnerabilities. Digital Twins enhance this analysis by simulating and analyzing different scenarios, by incorporating data from hydrological models, seismic sensors, weather forecast models, and historical incident reports. This capability is especially important for assessing the impact of natural disasters, such as floods and earthquakes, on mobility systems. By integrating and visualizing this data, Digital Twins help identify vulnerabilities and develop strategies for resilience and emergency response. However, a static Digital Twin has a limited lifespan and visualization ability. This presentation introduces the roadmap for a ‘Living Digital Twin’, a dynamic virtual counterpart of a live physical entity that can simultaneously support real-time traffic management, predictive maintenance, and safety enhancement. We will show how the Living Digital Twin can augment experts’ judgment and enhance readiness for diverse scenarios by harnessing the power of Artificial Intelligence (AI) and Machine Learning (ML). We will discuss various use cases of such framework in enhancing infrastructure analysis, transportation, disaster mitigation, and recovery programs, and highlight the possibilities in the integration of Cloud Infrastructure, GIS, AI, and ML to boost location intelligence analytics. Finally, we will present examples of how a Living Digital Twin can revolutionize data-driven decision-making processes across various domains.
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