$2M+ in projected operational savings through AI-powered urban traffic optimization on AWS

Urban traffic systems face growing challenges with increasing vehicle density and manual enforcement processes. Traditional methods limit efficiency in violation detection, fine processing, and congestion management. 

By integrating Generative AI and AWS-powered automation, cities can move towards predictive, AI-driven traffic management—enhancing compliance, reducing congestion, and improving operational efficiency.

Customer overview

A pioneer in intelligent traffic management, our customer specializes in automated violation detection, real-time incident tracking, and AI-driven traffic insights to create safer, more efficient roads. By leveraging advanced video intelligence and AI-powered automation, they aim to revolutionize urban mobility—enhancing enforcement accuracy, optimizing traffic flow, and ensuring compliance with VIDES standards.

Challenges | Smarter traffic management, not mere surveillance

Urban centers generate vast amounts of traffic data, but without AI-driven intelligence, this data remains underutilized.  Despite managing vast amounts of traffic data, their enforcement and congestion control systems were largely reactive—leading to inefficiencies, high operational costs, and delays in violation processing. 

A next-generation solution was required—one that could automate enforcement, analyze real-time traffic patterns, and optimize traffic flow at scale.

• Processing inefficiencies: Traditional manual review methods were slow and expensive, delaying critical enforcement actions.
• Lack of predictive capabilities: Without AI-powered forecasting, city planners struggled to optimize traffic control measures.
• Manual violation handling: The reliance on human intervention for ticket issuance and processing led to enforcement gaps and compliance issues.

Solution | AI-driven traffic enforcement and congestion management

We implemented a cloud-native AI solution on AWS for our customer, leveraging real-time analytics, deep learning models, and automation to create an efficient, scalable, and fully automated traffic enforcement system that complies with NHAI VIDES standards.

Generative AI-powered infrastructure on AWS

The traffic management system required a scalable, real-time AI solution to handle high-volume data ingestion, process violations efficiently, and enable predictive congestion control. 

The AWS solution processes high-volume traffic data in real time, applies machine learning for predictive insights, and ensures compliance with NHAI VIDES standards for automated enforcement.

• Automated traffic violation detection: Integrated Amazon Rekognition and YOLOv8 models on SageMaker for real-time license plate recognition and anomaly detection.
• Predictive traffic congestion analytics: Used DeepAR on SageMaker to forecast congestion trends and optimize traffic control strategies.
• AI-powered violation processing: Deployed AWS Lambda to automate e-Challan generation, reducing processing delays.
• RAG-based violation retrieval: Implemented Amazon OpenSearch for instant access to historical traffic violations and trends, enhancing law enforcement efficiency.

Success metrics

With AI at its core, traffic management has evolved from manual enforcement to intelligent, data-driven decision-making. More than just automation, it redefined efficiency, compliance, and cost-effectiveness.

• 40% reduction in manual processing costs – AI-driven violation detection and automated workflows minimized labor-intensive enforcement.
• 60% savings on infrastructure investments – Cloud-based AI models eliminated the need for costly on-premise GPU and server maintenance.
• 35% faster fine collection – Automated processing accelerated revenue realization, reducing delays in enforcement actions.
• $2M+ in projected operational savings over three years – Streamlined workflows and intelligent automation drove substantial cost reductions.
• 80% acceleration in violation processing – Faster detection and enforcement improved compliance rates and urban mobility.