Smart Traffic Control

Construction companies, municipalities, and road maintenance agencies commonly rely on manual flaggers or fixed timer-based signals to manage traffic in single-lane roadwork zones. These approaches expose workers to safety risks, particularly in high-speed, low-visibility, or remote environments, while also causing unpredictable delays and high operational costs. Legacy systems lack real-time visibility into congestion, queue lengths, and lane occupancy, and coordination between both ends of a work zone is often manual and error-prone. There was a clear need for an automated, cost-effective solution that could adapt dynamically to real-world traffic conditions without relying on cellular or wired infrastructure.

To meet this need, an AI-powered, fully autonomous single-lane traffic controller was developed using dual cameras, edge AI inference, and long-range LoRa communication. The system continuously monitors vehicle flow at both ends of the work zone, dynamically adjusts signal timing, and replaces fixed timers with intelligent, data-driven control. Designed for solar-powered operation, it supports rapid deployment in remote or infrastructure-limited locations, while integrated analytics provide real-time and historical insights into traffic behavior.

The solution significantly improves safety by eliminating the need for human flaggers and reducing coordination-related incidents. Adaptive traffic control reduces average waiting times by 20–40%, lowers staffing costs by 60–80%, and consistently delivers over 99% uptime in field deployments. A 90-day pilot recorded zero safety incidents, reduced average wait times from 4.8 to 2.9 minutes, and increased throughput by 27%, demonstrating a scalable and modern approach to safer, more efficient roadwork traffic management.