Gandhinagar Introduces AI Traffic Lights To Cut Congestion

12 May 2026 CW Team

The system integrates roadside cameras, loop detectors and infrared sensors with a central control unit that processes live feeds and adjusts signal timings. AI algorithms analyse vehicle density and queue lengths to optimise phase durations and minimise stoppage. The corporation said the networked approach allows prioritisation for public transport and emergency vehicles and supports data collection for future planning.

Officials reported that an initial pilot at five junctions cut average waiting times by 30 per cent and improved average travel speeds during peak hours. They said emissions from idling vehicles fell as stop–start cycles reduced and that traffic officers saw smoother dispersal when incidents occurred. The municipal team added that analytics dashboards will track performance indicators such as delay, throughput and compliance to refine control strategies.

The rollout is being extended in phases across key corridors and officials said training programmes have been arranged for traffic staff to operate and maintain the new infrastructure. The corporation indicated integration with existing city systems and said vendor support contracts include periodic calibration and software updates. Ongoing monitoring will inform expansion to additional neighbourhoods and enable adjustments to meet changing traffic patterns.

City officials said citizen feedback channels will be used to assess user experience and that data sharing with urban planners will support long term mobility measures. Performance results will be published periodically to inform stakeholders and guide subsequent deployments.

The Gandhinagar Municipal Corporation has introduced an Artificial intelligence driven smart signalling system that adjusts traffic lights according to real-time congestion levels across the city. The municipal administration said the system will redistribute green time dynamically to ease queues and manage traffic flows at major intersections. The launch was led by GMC Maharshi Raval as part of the city traffic modernisation programme. The system integrates roadside cameras, loop detectors and infrared sensors with a central control unit that processes live feeds and adjusts signal timings. AI algorithms analyse vehicle density and queue lengths to optimise phase durations and minimise stoppage. The corporation said the networked approach allows prioritisation for public transport and emergency vehicles and supports data collection for future planning. Officials reported that an initial pilot at five junctions cut average waiting times by 30 per cent and improved average travel speeds during peak hours. They said emissions from idling vehicles fell as stop–start cycles reduced and that traffic officers saw smoother dispersal when incidents occurred. The municipal team added that analytics dashboards will track performance indicators such as delay, throughput and compliance to refine control strategies. The rollout is being extended in phases across key corridors and officials said training programmes have been arranged for traffic staff to operate and maintain the new infrastructure. The corporation indicated integration with existing city systems and said vendor support contracts include periodic calibration and software updates. Ongoing monitoring will inform expansion to additional neighbourhoods and enable adjustments to meet changing traffic patterns. City officials said citizen feedback channels will be used to assess user experience and that data sharing with urban planners will support long term mobility measures. Performance results will be published periodically to inform stakeholders and guide subsequent deployments.

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