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.
Analyzing the Role of Arduino and LTE in IoT-Powered Adaptive Traffic Solutions
2024
Article (Journal)
Electronic Resource
Unknown
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