A joint research initiative between ARJUN and the Massachusetts Institute of Technology (MIT) has yielded its first set of highly promising results in urban traffic optimization for Gurugram and Faridabad.
Key Findings
Using real-time sensor data and AI-driven signal management via the HACF, the pilot project demonstrated significant improvements over a 30-day testing window:
- Reduced Commute Times: A 14% reduction in average commute times during peak morning and evening hours.
- Emission Reductions: A measurable decrease in localized nitrogen dioxide (NO2) and PM2.5 concentrations near historically severe traffic bottlenecks.
- Emergency Response: Optimized "green wave" routing for emergency response vehicles, cutting ambulance transit times by an average of 4 minutes.
The Technical Approach
The system uses a decentralized reinforcement learning model where individual traffic intersections communicate state data to neighboring nodes, creating a fluid, constantly adjusting grid rather than relying on rigid, pre-programmed timing plans.
Scaling the Solution
These findings will be used to scale the "Smart Grid" traffic management system across the entire National Capital Region (NCR) section of Haryana by early 2027.