Analyzing the Role of Arduino and LTE in IoT-Powered Adaptive Traffic Solutions

Abstract

Background: Urban traffic demands efficient management solutions to reduce congestion and improve flow. Traditional traffic signal systems, mostly static, struggle to track urban activity. Objective: This article uses IoT technologies, Arduino microcontrollers, and LTE connection to create an adaptive traffic light system that constantly adjusts traffic signal lengths to maximize traffic flow. Methodology: We created a prototype adaptive traffic light system using Arduino microcontrollers with LTE modules and sensors. The sensors send Real-time traffic data over LTE to a cloud server. The technology uses machine learning algorithms to assess data and traffic conditions and remotely alter traffic signal timings via IoT. Results: The prototype improved traffic flow and reduced congestion during peak hours at chosen junctions. In quantitative terms, traffic throughput rose 25%, and intersection waiting times decreased by 35%. Idling time reduction was anticipated to lower vehicle emissions. Conclusion: Arduino and LTE connection in an IoT-based adaptive traffic signal system show promise for urban traffic management. Traffic flow, waiting times, and emissions improve, proving its scalability and enabling cities to a sustainable and effective traffic management plan as vehicle loads rise. Further study is needed to determine its efficacy in different metropolitan topologies and traffic patterns.