For over a decade, India’s smart city narrative has been defined in terms of upgrading infrastructure, such as installing cameras and sensors at junctions, launching command-and-control centres, and deploying large-scale technology. While these projects improved visibility, they are largely reactive tools.
This is especially true when it comes to traffic management, with cameras observing congestion and prompting controllers to action only when the roads are jammed. Such static traffic management hasn’t helped Indian cities like Bengaluru, which remains the world’s third-most congested city, according to TomTom Traffic Index 2025.
That model is now quietly breaking down.
Across leading Indian cities, urban traffic management technology is shifting from asset-heavy smart city projects to predictive, AI-led operations. Both the Pune Expressway and the Dwarka Expressway have installed advanced traffic management systems to improve traffic control, safety, and violation detection. Meanwhile, Bengaluru updated its AI-driven Adaptive Traffic Control System this year to streamline signal timings.
Speaking to AIM, Arcadis IBI Group director Venkata Subbarao Chunduru, who has over 25 years of experience in traffic and transportation system engineering, said AI transforms traffic management from reactive control to predictive governance. “The future of traffic management lies in city-owned data, AI-driven intelligence, and cloud-based platforms that scale outcomes, not hardware,” he added.
It enables real-time situational awareness by continuously assessing congestion, queues, incidents, and violations, and delivering predictive intelligence to forecast congestion, accidents, weather disruptions, VIP movement effects, and event-driven traffic surges.
At the operational level, AI powers adaptive control, dynamically adjusting signal timings, diversion strategies, and enforcement priorities in response to live conditions and provides operational decision support by recommending specific actions to traffic police rather than merely presenting static dashboards.
It also strengthens performance management by objectively measuring the impact of interventions at the junction, corridor, and city levels.
“The biggest shift is that AI augments human decision-making rather than replacing it, enabling traffic police and city authorities to act faster, earlier, and more confidently,” Chunduru added.
Chunduru mentioned that there is no single model that fits all cities. The most effective approach is a hybrid, federated model that balances local control with scalable technology. In this framework, core traffic intelligence and governance must remain city-owned, because traffic management is fundamentally a public safety and urban governance function. At the same time, cities must partner with cloud-based platforms.
Public–private partnership (PPP) models work best for delivering services, believes Chunduru. However, they are not particularly suited for data ownership, particularly for operations, analytics, and performance-linked services, due to information asymmetry and conflicting priorities. While AI platforms should be cloud-native, interoperable, and shared, private partners should be incentivised on outcomes, not assets, he noted.
This model ensures data sovereignty and accountability, enables rapid innovation and scalability, delivers cost efficiency, and avoids vendor lock-in.
AI Traffic Management in Bengaluru
Priyank Kharge, Karnataka’s IT-BT and Rural Development and Panchayat Raj minister, suggested at a Confederation of Indian Industry summit earlier this year that traffic congestion is a byproduct of the city’s accelerated development. According to a CBRE report, Bengaluru’s tech sector employment grew 12% between 2018 and 2023. The Bengaluru Innovation Report, released this year, projects average annual growth of 8.5% over the next decade.
While launching a traffic quality index, he observed that the city incurs an estimated annual loss of ₹20,000 crore due to time wasted in traffic. In response, the city implemented the Bengaluru Adaptive Traffic Control System (BATCS) that uses computer vision to monitor and adjust traffic light timings based on real-time vehicle counts.
“In Bengaluru, we are implementing traffic intelligence as an AI-led operational system, not as a technology project,” Chunduru explained.
The Bengaluru approach leverages existing infrastructure—such as CCTV networks, integrated traffic management systems, automated number plate recognition, adaptive signals, and command centres—without adding new hardware. BATCS’ unified AI intelligence layer that harnesses that data and follows a closed-loop model of detect → decide → act → measure impact.
AI-based adaptive signal control has already had a measurable impact in Bengaluru. According to Seemant Kumar Singh, Commissioner, Bengaluru City Police, the BATCS system, scaled to 169 junctions, has led to a 15–20% reduction in travel time and a 20% increase in corridor throughput.
“This effort is not merely about deploying new technology; it is about institutionalising smarter ways of managing traffic. Each junction is calibrated based on road geometry, surrounding land use, pedestrian behaviour, and enforcement bottlenecks,” he wrote in Deccan Herald.
Tackling Traffic Chaos in Chhatrapati Sambhaji Nagar
Similarly, SecuTech Automation undertook the Smart Traffic Management project in Chhatrapati Sambhaji Nagar, formerly Aurangabad. Unlike metros, where some level of traffic discipline is enforced, the city presented a far more complex challenge.
Aditya Prabhu, the company’s group CEO and technology lead, told AIM he found traffic signals were often switched off due to inefficient, non-adaptive timing and operated manually during peak hours, creating chaos at key junctions. Moreover, two-wheeler violations were rampant, compromising road safety.
“Safety to human life was our primary concern, and this was possible only through enforced traffic discipline,” Prabhu said. “Enforced discipline had to be done using technology without relying on any human intervention.”
SecuTech’s solution reframed traffic management as a data and AI problem rather than a hardware one.
At the edge, cameras were reimagined as data collection devices rather than visualisation devices, extracting metadata on vehicles, people, license plates, and behaviour. AI-driven image recognition models applied rules to detect violations such as red-light jumping, zebra-crossing violations, wrong-way driving, speeding, and helmet compliance.
Smart Traffic Management applied machine learning to identify repeat offenders, congestion patterns, and time-based traffic behaviour. This enabled adaptive signal control—dynamically changing signal timings based on real-time demand rather than static schedules.
Beyond enforcement, the platform delivered a humanless evidence management system, where citizens could see exactly where and why a challan was issued and pay online. Its AI was also used for crime analysis, such as tracking stolen vehicles across the city’s camera network, and to address citizen concerns, such as waste management.
Solving for Local Complexity
One of the most unique challenges was attuning the AI model for local use. In Chhatrapati Sambhaji Nagar, many license plates are handwritten in Marathi.
“How can I capture a license plate written in Devanagari script, convert that to English, and raise a fine?” Prabhu recalled navigating the problem.
The answer was AI-powered optical character recognition. “With 70–80% accuracy… detection and translation happen automatically. Only correction becomes a human intervention,” he said, talking about reducing reliance on manual processes.
The result in Chhatrapati Sambhaji Nagar has been tangible: improved traffic discipline, reduced accidents, lower congestion, and increased state revenue from violations, Prabhu claimed.
The project was government-commissioned and funded, but Prabhu believed new investment models would emerge. “Every model will evolve based on the economic challenges that it has solved,” he said, drawing parallels with road infrastructure PPPs.
For SecuTech, Chhatrapati Sambhaji Nagar is proof that AI-led, platform-driven traffic governance can scale across India, provided cities move beyond products and focus on outcomes.
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