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Urban Mobility

How Nagpur Uses AI to Reduce Traffic and Travel Time

Nagpur's IITMS project deploys AI-powered adaptive traffic signals to dynamically manage traffic, reducing congestion and improving travel efficiency.

By Editorial Team
April 2, 2025

Nagpur is implementing an AI-powered adaptive traffic signal system designed to reduce travel time and congestion by dynamically adjusting traffic lights based on real-time vehicle flow. The project is part of the city’s broader Intelligent and Integrated Traffic Management System (IITMS), with a pilot phase already underway at 10 key intersections.


Key Facts

  • Location: Nagpur, India
  • Sector: Urban Mobility / Traffic Management
  • AI Application: Adaptive traffic signal control based on real-time flow
  • Technologies Used: AI, IoT sensors, real-time analytics, LED-based signage, speed radars
  • Technology Partner(s): Keltron (Kerala State Electronics Development Corporation)
  • Timeline: Pilot phase ends April 2025; full rollout over 10 months
  • Lead Stakeholders: Nagpur Municipal Corporation (NMC), Nagpur Traffic Police
  • Funding: ₹197 crore (~$26 million USD)


Project Context

Nagpur has faced increasing congestion due to rising vehicle numbers and urban expansion. Traditional timer-based traffic lights have proven inadequate in managing peak-hour traffic and are often blind to actual conditions on the ground. To modernize its mobility infrastructure, the city launched the IITMS project, beginning with a pilot at high-traffic intersections like Kachipura, Bajaj Nagar, Shankar Nagar, and Alankar Talkies. These locations were selected based on detailed traffic analysis and supervision by Deputy Commissioner of Police (Traffic), Archit Chandak.

The new system replaces static timers with real-time, AI-based decision-making that adjusts signal phases based on actual traffic volume. This allows smoother flows, especially during peak hours, and significantly reduces idling time, fuel waste, and commuter frustration.

Implementation

The IITMS system integrates multiple layers of smart infrastructure. At the core are AI-powered signal controllers that respond to vehicle flow in real time. Data is collected through a combination of IoT sensors, radars, and traffic cameras. Adaptive algorithms process this input to determine how long each light stays green or red, constantly optimizing for efficiency.

Speed radars and LED-based digital signage are being installed to inform and guide drivers. The system is also compatible with automatic number plate recognition and red-light violation detection technologies, laying the groundwork for future traffic enforcement and analytics tools. The pilot will inform final calibration before full deployment across 161 intersections over the following 10 months.

Impact

Initial projections from the pilot suggest a potential 28–48% reduction in travel time and a 46–62% increase in average vehicle speeds. These improvements are expected to have compounding effects: reducing emissions, improving air quality, and enhancing day-to-day life for commuters. The centralized data stream also equips city planners and law enforcement with tools to monitor traffic patterns, respond to incidents faster, and enforce rules more effectively. Ultimately, the system is expected to shift Nagpur toward a more responsive, data-informed model of urban mobility.

Why It Matters

Nagpur’s adoption of AI-driven traffic control demonstrates how a mid-sized city can take a leap forward in urban governance using available technologies. Rather than waiting for congestion to worsen, the city is investing early in adaptive solutions that treat mobility as a living system—adjusting as conditions change. By moving beyond outdated traffic light timers, Nagpur positions itself as a national leader in smart mobility, showing other Indian cities what’s possible when data and infrastructure align.

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