AI on the Roads: Can Technology Solve India’s Traffic Challenges?

As urbanization intensifies, traffic congestion has become one of the most pressing challenges for modern cities. In India, where vehicle ownership is soaring and infrastructure is struggling to keep pace, cities like Bengaluru, Mumbai, and Delhi rank among the world’s most congested. Congestion, erratic driving behaviors, and ineffective enforcement systems plague urban mobility. Artificial Intelligence (AI) offers a powerful solution to these challenges, but can AI alone fix the deeply ingrained traffic woes of Indian cities?  Venkata Subbarao Chunduru, Director, Arcadis Group, elaborates.

Indian cities face severe traffic challenges driven by both infrastructure and human factors. Rapid urbanization, paired with unchecked vehicle growth, has led to gridlocked roads, with cities like Bengaluru witnessing up to 71% more travel time during peak hours, according to the TomTom Traffic Index. Beyond congestion, erratic driving behaviors, poor lane discipline, and a disregard for traffic rules add to the problem. Ineffective enforcement, often resulting from weak political will and inconsistent implementation, further exacerbates the issue. The environmental impact is also significant. The Centre for Science and Environment reports that road transport contributes 40% of particulate matter emissions in cities like Delhi, contributing to dangerous levels of air pollution. With around 150,000 road fatalities annually—many due to preventable causes like reckless driving and inadequate enforcement—Indian cities urgently need innovative solutions that go beyond conventional methods.

AI must evolve to become part of a broader urban mobility ecosystem, working in tandem with human decision-makers, urban planners, and policymakers. When combined with public engagement and inclusive design, AI can help cities achieve not just smoother traffic flow, but safer, more efficient, and sustainable urban environments for all.

–  Venkata Subbarao Chunduru

These escalating traffic challenges in Indian cities demand a comprehensive approach from traffic authorities that integrates domain expertise, technology, and collaborative governance. This three-pronged strategy is essential for creating a holistic and effective urban traffic management plan.

Domain Expertise: The Foundation

Traffic management requires a deep understanding of traffic dynamics, including flow analysis, road safety, signal optimization, and public transport planning. Professionals must be well-versed in these areas to effectively address the unique challenges posed by Indian cities, such as mixed traffic, diverse vehicle types, and erratic driving behaviors. Additionally, expertise in electronics and communication engineering is crucial for designing, deploying, and maintaining the hardware infrastructure required for Intelligent Traffic Management Systems (ITMS).

Technology and Data Analytics: The Enabler

While domain expertise provides the foundation, technology, data analytics, and AI are the enablers. Advanced AI algorithms can predict traffic patterns, optimize traffic flow, and identify bottlenecks proactively. Real-time data from sensors and connected systems empowers traffic authorities to make informed decisions on traffic control, incident management, and long-term urban mobility planning.

Collaboration: The Catalyst

Effective traffic management necessitates collaboration among government bodies, urban planners, traffic police, technology providers, public transport authorities, and the general public. By working together, stakeholders can ensure that traffic management solutions are integrated, aligned with broader urban mobility goals, and supported by necessary laws and frameworks.

These three pillars—domain expertise, technology, and collaboration—are interdependent and must work together seamlessly. Domain skills ensure that AI systems are designed with a deep understanding of traffic dynamics. Technology and AI enhance these systems by providing real-time insights and predictive capabilities. Collaboration ensures that solutions are aligned with broader urban mobility goals and policies. By leveraging these three pillars, traffic authorities can shift from reactive to proactive traffic management. This integrated approach can lead to smoother traffic flows, enhanced road safety, and a more sustainable urban environment.

While human factors, infrastructure, and policy play significant roles, technology, particularly AI, offers promising solutions to these complex challenges. By leveraging AI’s capabilities, cities can optimize traffic flow, improve safety, and reduce congestion in a more efficient and sustainable manner.

Promise of AI in Traffic Management

AI excels at processing massive amounts of real-time data to provide predictive and adaptive solutions. Below are several use cases and successful implementations, both globally and within India, showcasing the impact of AI in traffic management.

1. AI for Traffic Optimization:  Adaptive Traffic Signal Control (ATSC):  Adaptive Traffic Signal Control (ATSC) systems use AI to adjust signal timings based on real-time traffic conditions. In Pittsburgh, the Surtrac system reduced travel times by 25% and vehicle emissions by 21%. Similarly, Hyderabad has implemented an AI-driven adaptive traffic control system that has reduced travel times by 18% at busy intersections, easing congestion and setting a model for other cities in India.
2. AI for Traffic Safety: Incident Detection and Prevention: Singapore’s Smart Nation Initiative integrates AI and data analytics to detect and respond to road incidents in real-time. This system alerts emergency services to accidents, stranded vehicles, or breakdowns for faster response. In India, Delhi’s AI-based road safety system uses video analytics to detect violations such as speeding and red-light jumping, automatically issuing e-challans (electronic fines), enhancing enforcement, and improving road safety.
3. AI for Public Transportation: Predictive Transit Optimization: In London, AI systems predict bus arrival times and dynamically adjust routes to reduce commuter wait times. In Mumbai, AI-powered predictive maintenance systems for the city’s bus fleet have minimized breakdowns and improved service reliability, making public transportation a more efficient option.
4. AI for Congestion Management: Real-Time Traffic Predictions: In Barcelona, AI-driven predictive analytics help forecast traffic patterns and dynamically adjust signal timings to prevent congestion before it occurs. In Bangalore, AI-based congestion monitoring systems analyze real-time traffic data to predict and mitigate congestion at critical junctions. This has reduced travel times by up to 15% in specific corridors.
5. AI for Toll and Revenue Leakage Monitoring: India’s National Highways Authority (NHAI) has implemented AI-powered systems to monitor toll booths and detect revenue leakage. By using cameras and sensors to capture vehicle details, these systems ensure that toll payments are accurate and reduce revenue losses from fraud.
6. AI for Environmental Impact Reduction: Eco-Driving Solutions: Amsterdam’s AI-driven Eco-driving initiative helps drivers adopt more fuel-efficient behaviors by providing real-time feedback. This reduces emissions and fuel consumption. In Delhi, AI systems are being explored to link traffic control with air quality management, adjusting signal timings to reduce vehicle idling and emissions, a vital step for heavily polluted cities.

AI-Powered Traffic Management in Bangalore (ASTraM Initiative)

ASTraM (Actionable Intelligence for Sustainable Traffic Management) is an advanced traffic management solution designed to optimize urban mobility through the integration of data analytics, AI, and multi-agency collaboration. It provides real-time situational awareness by aggregating data from various sources, including sensors, cameras, and crowd-sourced inputs. ASTraM offers modules for incident reporting, congestion monitoring, and simulation-based planning, allowing authorities to predict and mitigate traffic challenges proactively. The platform also facilitates collaboration among key stakeholders such as traffic police, municipal bodies, and emergency services, ensuring a coordinated response to traffic incidents and events. The system’s capabilities include AI-driven congestion prediction, scenario simulation, and the use of dashboards for performance monitoring and analytics, enabling authorities to implement effective and timely traffic management strategies.

ASTraM has demonstrated its potential to address complex urban traffic issues in Bangalore, where it provides real-time insights to traffic officers, optimizing signal timings, improving congestion management, and enhancing incident response, especially during special events and at critical junctions. The platform’s holistic approach, which combines AI-based predictive modeling, real-time data integration, and strong stakeholder collaboration, makes it a powerful tool for sustainable and effective traffic management in Indian cities and beyond. The success of ASTraM illustrates how AI solutions, when tailored to local needs, can significantly improve urban mobility. This initiative serves as a model for scaling AI-driven traffic management across other Indian cities facing similar challenges.

Key Failures and Learnings from AI Deployments

Despite AI’s potential, early deployments reveal several challenges that must be addressed:

1. Data Quality and Integration: Poor-quality data or insufficient real-time data from traffic sensors and IoT devices can undermine the accuracy and effectiveness of AI systems.
2. Infrastructure and Scalability: Many cities lack the infrastructure needed to fully support AI systems, making scalability a challenge.
3. Over-reliance on Technology: Focusing too much on AI without considering human factors like driving behavior can limit its effectiveness. In cities like Bangalore, AI systems alone have not fully alleviated congestion due to non-compliance with traffic laws.
4. High Costs and Maintenance: AI systems are expensive to implement and maintain, often becoming obsolete without sustained investment in infrastructure and technology upgrades.

The Need for a Holistic Approach Beyond AI

While AI has proven to be a powerful tool, it cannot be viewed as a standalone solution. A truly effective traffic management strategy requires a holistic, multi-dimensional approach that integrates AI with infrastructure improvements, human behavioral interventions, regulatory reforms, and collaborative governance. The challenges Indian cities face—congestion, pollution, road safety—are complex, and addressing them requires the following pillars:

1. Integrated Infrastructure Development: AI systems can only be effective if supported by modern infrastructure, such as well-designed roads, public transport networks, and properly maintained sensors and cameras. Cities need significant investment in infrastructure that complements AI solutions.
2. Behavioral Change and Enforcement: Human driving behavior plays a pivotal role in traffic management. AI systems, while capable of optimizing traffic flows, cannot enforce compliance without proper regulatory support. Consistent enforcement of traffic laws, supported by AI, is necessary to change driver behavior and improve road safety.
3. Strong Political and Institutional Support: AI deployments require robust governance and political commitment. Policymakers must establish clear frameworks that include data privacy protections, regulatory oversight, and long-term funding for AI systems and infrastructure. Without political will, even the best AI systems can fail due to lack of resources or poor implementation.
4. Public Engagement and Inclusivity: Public acceptance of AI-driven traffic management systems is critical to their success. Transparent communication about how AI systems work and their benefits—such as reducing congestion and improving safety—will help build trust. Furthermore, AI systems must be designed with inclusivity in mind, ensuring that they benefit all sections of society, including low-income communities reliant on public transport.

Evolving AI for the Future of Urban Mobility

The future of AI in traffic management lies in greater integration, adaptability, and innovation. As AI technology evolves, its role in traffic management will expand beyond real-time optimization to include predictive urban planning, personalized traffic solutions, and multi-modal transportation management. Here’s how AI can evolve to address current limitations:

Urban Planning Integration: AI should work hand-in-hand with urban planners to design smarter cities that anticipate future traffic needs. AI-driven predictive models can help optimize the placement of new roads, public transport lines, and pedestrian-friendly infrastructure.

Collaborative AI Ecosystems: AI needs to be part of a broader ecosystem that integrates data across various domains—transportation, environmental monitoring, emergency services—to create a seamless urban mobility system that prioritizes safety, sustainability, and efficiency.

Personalized, Real-Time Solutions: As AI becomes more sophisticated, systems can offer individualized traffic solutions for drivers, helping to reduce congestion through dynamic routing, carpooling recommendations, and real-time parking updates.

Inclusivity and Accessibility: AI systems must evolve to bridge the digital divide, ensuring that even the most marginalized communities benefit from improved traffic management. By optimizing public transport routes and accessibility, AI can help cities become more equitable.

The Road Ahead for AI in Traffic Management

AI represents a game-changing opportunity to tackle the persistent traffic challenges faced by Indian cities. But it is not a panacea. To fully unlock the potential of AI, a balanced and holistic approach is required—one that integrates AI with infrastructure investments, human behavioral changes, regulatory frameworks, and strong governance. As cities like Bangalore and Hyderabad demonstrate the effectiveness of AI systems like ASTraM, the lessons learned from these implementations will guide future deployments.