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Sensor-informed Generative Digital Twin: High-fidelity Simulation for Sustainable Transportation and Policy Validation

Understanding the behaviors of vehicles and other traffic participants at busy urban intersections is critical for urban planning, infrastructure development, and policymaking. Unfortunately, such understanding often comes after a huge investment for implementation and deployment. Many complex interactions occur infrequently and are difficult to capture through after-deployment monitoring. This project will develop a sensor-informed generative digital twin that integrates real-world data from the Riverside Innovation Corridor’s sensor network. By continuously integrating real-time sensory inputs, the platform can be used to create high-fidelity scenarios and simulate rare and challenging transportation dynamics. The digital twin will serve as a decision-support tool for policy evaluation, traffic efficiency strategies, and urban mobility planning. Its predictive capabilities will assist in designing infrastructure for autonomous vehicles, optimizing multi-modal travel demand, and enhancing energy efficiency. Through engagement with policymakers and stakeholders, the project will pave the foundation for the digital twin’s application in real-world decision-making. The proposed research will serve as a bridge, connecting data-driven insights with policy implementation towards sustainable transportation systems.

Language

  • English

Project

Subject/Index Terms

Filing Info

  • Accession Number: 01985471
  • Record Type: Research project
  • Source Agency: National Center for Sustainable Transportation
  • Contract Numbers: DOT 69A3552348319, DOT 69A3552344814
  • Files: UTC, RIP
  • Created Date: Apr 12 2026 11:41PM