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SMART-TWIN: Systematic Modeling and Real-Time Transportation with Digital Twins
The SMART-TWIN project aims to transform transportation systems through the development of Digital Twin (DT) technology—virtual models that replicate real-world infrastructure using real-time data, sensors, and AI. These models allow transportation planners to simulate traffic conditions, predict disruptions, and optimize operations without impacting physical systems. As urbanization and traffic congestion grow, DTs offer innovative solutions for safer, more efficient, and sustainable transportation planning.
The project includes three phases: designing a DT architecture, developing applications for traffic and emergency management, and testing solutions through real-world case studies. The results will demonstrate improved efficiency, safety, and cost savings for local and regional agencies.
Beyond research, SMART-TWIN supports workforce development by engaging students in hands-on experience with cutting-edge technologies, preparing them for careers in smart infrastructure. The project will position Fresno State and its partners as leaders in data-driven, intelligent transportation systems
Principal Investigator:
Hovannes Kulhandjian, PhD
PI Contact Information:
hkulhandjian@csufresno.edu
California State University, Fresno
Dates:
March 2025 to March 2026
Implementation of Research Outcomes:
The SMART-TWIN project is expected to produce a range of impactful research outputs that advance the field of intelligent transportation systems. Key deliverables include:
Digital Twin Framework and Methodology: A scalable, modular framework for the design, development, and deployment of transportation-specific Digital Twins, adaptable to various urban and rural scenarios.
Simulation Models and Tools: High-fidelity simulation models for real-time traffic flow, emergency response, and infrastructure planning using platforms such as AnyLogic, SUMO, and MATLAB Simulink, integrated with visualization tools like Unity and Unreal Engine.
Custom Software Prototypes: AI-enabled software applications for congestion prediction, dynamic traffic signal control, smart parking, and emergency routing, leveraging IoT data and edge-computing platforms (e.g., NVIDIA Jetson Orin).
Technical Reports and Guidelines: Detailed documentation and implementation guides for transportation agencies to adopt and scale Digital Twin technologies, including performance benchmarks and best practices.
Impacts/Benefits of Implementation:
The implementation of the SMART-TWIN project is expected to bring transformational benefits to transportation systems through the integration of real-time data, predictive analytics, and simulation into planning and operational decision-making. By enabling transportation agencies to model and simulate scenarios before taking action, Digital Twin (DT) technology will lead to safer, more efficient, and cost-effective infrastructure management.
Key anticipated impacts include:
Improved Safety: Real-time simulation and AI-driven predictive models will enhance emergency preparedness and response, reducing incident severity and improving response times for first responders.
Reduced Congestion and Travel Delays: Dynamic traffic signal control and congestion prediction will optimize traffic flow, reduce bottlenecks, and improve commuter reliability.
Cost Savings and Operational Efficiency: Predictive maintenance and simulation-based planning will lower infrastructure maintenance costs and extend the lifespan of transportation assets.
Environmental Sustainability: Optimized routing and traffic management will reduce emissions and energy consumption, supporting state and national sustainability goals.
Policy and Planning Advancements: The project will generate tools and data that can inform transportation policy, regional planning, and funding decisions, supporting smarter infrastructure investment strategies at the local and state levels.
Technology Transfer and Innovation: Potential intellectual property, including software prototypes and patentable DT architecture, will serve as a platform for further innovation, commercialization, and broader adoption by transportation agencies.
Collectively, the SMART-TWIN project will provide a scalable, data-driven approach to modernizing transportation systems, resulting in improved safety, reliability, resilience, and sustainability for communities and agencies alike.
Project Number:
2537
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Contact Us
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