As India accelerates highway construction, the focus is increasingly shifting to how the expanding network is managed once roads and expressways are operational. Rising traffic, frequent violations, breakdowns and accidents are putting greater pressure on highway authorities and operators to monitor roads and respond to incidents quickly.
Digital technologies such as artificial intelligence (AI), machine learning, computer vision and automated traffic management systems are increasingly being considered for these tasks. Industry executives say such systems can reduce the burden of continuous manual monitoring, but their wider use will also depend on the availability of reliable infrastructure, maintenance and common standards.
Highway operations face growing pressure
Anoop G Prabhu, Co-Founder and CTO at Vehant Technologies, told FinancialExpress.com that the operational challenge is no longer limited to adding more road capacity. Highway authorities also need to improve traffic flow, enforcement and incident response as traffic volumes increase and networks become more complex.
Prabhu said the growing size of the network is making it harder to rely only on manual monitoring for violations and incidents. AI-based systems can help identify events across multiple stretches and alert control rooms for further action.
How AI can improve traffic monitoring
AI-based systems can continuously analyse live video feeds to identify traffic violations and unusual activity on roads. These can include overspeeding, wrong-way driving, lane violations, mobile phone use while driving, seatbelt violations, triple riding and helmet violations.
Such monitoring can reduce the need for personnel to manually watch every camera feed and allow control-room teams to focus on incidents requiring assessment and response.
Prabhu also pointed to the use of AI for identifying accidents, stalled vehicles, road blockages, abandoned objects, pedestrians or animals entering highways, smoke or fire and unusual congestion.
Why faster incident detection matters
The time taken to identify an incident can directly affect how quickly assistance reaches the location. An early alert can allow traffic management centres and emergency teams to assess the situation and begin coordinating a response sooner.
“Absolutely. AI has fundamentally changed how highways are monitored by enabling authorities to move from reactive to proactive incident management. AI continuously analyzes live video feeds to detect accidents, stalled vehicles, pedestrians or animals entering highways, roadside debris, wrongway driving, low-visibility conditions, and abnormal congestion in real time.”
High-speed corridors are the biggest test
The case for faster detection is stronger on high-speed expressways and tunnels, where an accident, stalled vehicle or obstruction can quickly affect other road users.
Hare Krishna, CEO of Capital Infra Trust, told FinancialExpress.com that conventional CCTV monitoring becomes difficult when operators have to track hundreds of kilometres using limited staff.
“As India builds highways faster than ever, the real test is now shifting to how well we run them day to day. Operators are stretched thin trying to monitor long stretches for violations, breakdowns, and congestion, often with limited staff on the ground. AI is proving useful here, smart cameras and sensors can spot a wrong-side driver or a stalled vehicle instantly, without waiting for someone to notice on a screen. It doesn’t replace human oversight, but it takes over the repetitive watching, so teams can focus on actually responding rather than just monitoring.”
Krishna said the first few minutes after an accident are particularly important, making faster detection useful on high-speed corridors and tunnels.
“It genuinely can. With traditional CCTV setups, someone has to be watching the right screen at the right moment, which just isn’t realistic over hundreds of kilometres. AI-based systems flag incidents almost as they happen and alert control rooms directly, cutting out that lag between something going wrong and help being sent. That matters most in the first few minutes after an accident. We’ve seen this make the biggest difference on high-speed expressway stretches and tunnels, where things go wrong quickly and there’s simply no way for a human to watch every camera feed continuously.”
Cost, connectivity and maintenance remain hurdles
The wider deployment of AI-based traffic management systems faces several practical constraints. These include the cost of installing and operating the technology, the availability of reliable power and connectivity, and the regular maintenance of cameras and other field equipment.
Prabhu said procurement decisions also need to consider how systems perform over their full operating life rather than focusing only on the initial price.
“From our experience, 3 key areas can accelerate the adoption of AI-based traffic management across highways.
First, procurement should focus on long-term operational outcomes and lifecycle value rather than only the lowest upfront cost. Second, technical evaluation criteria need to evolve to assess AI systems on parameters such as accuracy, reliability, real-world performance, and scalability, rather than conventional methods alone. Third, periodic maintenance of field infrastructure is critical, as camera health and data quality directly impact AI performance. Clearer performance-based procurement frameworks, standardized technical benchmarks, and defined maintenance protocols can help create a more sustainable and effective ecosystem for AI-led highway management.”
Common standards needed for wider adoption
Infrastructure gaps are only one part of the challenge. Highway operators, police, emergency services and traffic management centres may use different systems, making the sharing of information and coordination of response more difficult.
Krishna said the lack of common standards for systems to communicate with each other can reduce the benefits of faster incident detection. He also pointed to the need to upgrade older highway stretches where the infrastructure required for AI-based systems may not already exist.
“What would really help is NHAI setting baseline tech standards for new projects, some support for retrofitting older highways, and clearer rules on how data gets shared across agencies so response isn’t held up by red tape.”
What the shift means for highway management
The move towards AI-based highway monitoring is therefore less about replacing human operators and more about helping them manage a network that is becoming longer, busier and harder to monitor manually.
Faster detection can help bring incidents to the attention of control rooms sooner, but the final response still depends on available personnel, emergency equipment, road connectivity and coordination between agencies.
As India’s highway network expands, the next challenge will be to ensure that digital monitoring systems are backed by reliable field infrastructure, clear operating standards and response mechanisms that can act on the information they generate.