Overview
This research develops advanced computational frameworks for optimizing traffic flow and transportation network management in dynamic urban environments — creating adaptive systems that make real-time decisions while accounting for long-term infrastructure planning, minimizing congestion, and improving overall network performance.
Technical Approach
- Scalable algorithms for large-scale network optimization problems.
- Machine learning integration to learn traffic patterns and predict optimal routing strategies.
- Dynamic network modeling representing urban transportation as adaptive, time-varying systems.
- Multi-objective optimization balancing efficiency, sustainability, and equity in transportation planning.
Expected Outcomes
Scalable computational tools for urban traffic optimization, improved understanding of dynamic traffic behavior in complex networks, and practical frameworks for transportation authorities and urban planners pursuing sustainable mobility.
