Adaptive IoT Traffic Management Utilizing MQTT Protocol Reduces Vehicle Wait Times
DOI:
https://doi.org/10.21070/jicte.v10i1.1727Keywords:
Smart traffic management, MQTT, Internet of Things, communication engineering, wireless sensor networksAbstract
Urban traffic congestion remains a continuous issue in today's urban areas as fixed time traffic signal plans do not have the ability to adapt to changes in traffic flow, queue length, or demand on the roadway. In this paper, we describe the development and implementation of an IoT based smart traffic control system utilizing the MQTT protocol for real-time communication and network optimization. Our system utilizes embedded traffic sensor nodes, wireless communication networks, an MQTT broker, a centralized decision-making server, and a monitoring display. At each traffic node, low-cost sensors determine if there are vehicles present or if there is a large amount of vehicle queueing. Once determined, the node will then publish this information to specific MQTT topics. The centralized server will subscribe to these MQTT topics and based on what it learns about the current traffic condition, it will then publish a command to change the signal timing. To assess how well our system performs from a communication engineering standpoint, we measured latency, packet delivery ratio, throughput, and scalability. Results showed that with our MQTT-based system, we achieved an average latency of 110ms; we had a packet delivery ratio of 98%; and we were able to reduce the average waiting time for drivers by 38.8% when compared to a traditional fixed time traffic control setup. Additionally, we also introduced an optional AI enhanced feature utilizing either a CNN or YOLO based detector to assist in accurately counting vehicles and classifying vehicles from camera feeds. Overall, our findings demonstrate that utilizing a lightweight publish-subscribe communication framework can create a practical, affordable, and scalable framework for implementing intelligent transportation systems within smart city environments.
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