What question did this study set out to answer?

This research aims to improve traffic signal control systems to reduce congestion and emissions in urban areas.

July 11, 2026Open Access

SYTRAC: An Edge AI-Based Intelligent Traffic Signal Control System Using OPC UA and Deep Learning for Smart City Applications

FBFares BouriachiUniversity of Sciences and Technology Houari Boumediene NDNacereddine DjelalUniversity of Sciences and Technology Houari Boumediene BKBadreddine KanouniUniversity Ferhat Abbas of Setif

Key Points

This research aims to improve traffic signal control systems to reduce congestion and emissions in urban areas.
Developed SYTRAC, a low-cost adaptive traffic control system for urban environments.
Utilized real-time vehicle detection with an NVIDIA Jetson Nano GPU and a Siemens PLC for safety execution.
Validated the system in hardware-in-the-loop field deployments at a live intersection.
Achieved a 22.1% reduction in average vehicle delay (p<0.001).
Microscopic simulations showed up to 28.0% delay suppression during lane-blockage incidents.
The system resulted in environmental savings of 53.5–72 kg of CO2 avoided per day.

Abstract

Urban traffic congestion is a primary driver of greenhouse gas emissions, wasted fuel, and degraded air quality, presenting a significant barrier to achieving sustainable cities (SDG 11) and climate action (SDG 13). Standard Adaptive Traffic Signal Control (ATSC) systems are either financially prohibitive for developing countries or lack certified safety mechanisms for physical deployment on live roads. This paper proposes and validates SYTRAC (System for Adaptive Traffic Control), a low-cost, safety-critical Adaptive Traffic Signal Control system designed for resource-constrained urban environments. SYTRAC implements an asynchronous co-design that combines real-time visual vehicle detection on an NVIDIA Jetson Nano GPU with deterministic safety execution on a Siemens S7-1200 Programmable Logic Controller (PLC). The core of the system is the Density-Weighted Adaptive Green Extension (DWAGE) algorithm. DWAGE provides a stable, interpretable, and computationally lightweight alternative to complex optimization methods such as genetic algorithms, particle swarm optimization, or Deep Reinforcement Learning. We establish a formal mathematical queue-stability guarantee using a closed-form Foster–Lyapunov drift argument. A three-mode fault-tolerant state machine with a 2 s watchdog automatically transitions to fixed-time fallback in the event of hardware or camera stream failures, protecting physical intersection safety. The system was validated through hardware-in-the-loop field deployments at a live intersection in Ouargla, Algeria. SYTRAC achieved a statistically significant 22. 1% reduction in average vehicle delay (p<0. 001), while microscopic simulations confirmed up to 28. 0% delay suppression during lane-blockage incidents. Critically, this delay reduction translates to an environmental saving of 53. 5–72 kg of CO2 avoided per day, alongside annual fuel savings of 8430 L. Assembled within a 1257 hardware budget, SYTRAC delivers a cost-effective, open-source, and reproducible platform that bridges the gap between adaptive intelligence and industrial safety, providing a scalable blueprint for sustainable urban traffic management in emerging economies.

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