Design of an IoT-Based Smart Traffic Control System for Reducing Congestion in Jos North LGA, Plateau State
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Background of the Study :
Urban traffic congestion poses significant challenges in many Nigerian cities, leading to increased travel times, pollution, and reduced economic productivity. In Jos North LGA, Plateau State, traffic management is hindered by inefficient signal systems and the absence of real-time monitoring. IoT-based smart traffic control systems offer an innovative solution by continuously collecting data from traffic sensors and cameras, allowing dynamic control of traffic signals to optimize flow. This study proposes the design of an IoT-based smart traffic control system that integrates vehicle detection sensors, speed monitors, and real-time analytics to adjust signal timings according to traffic conditions (Ibrahim, 2023). The system will employ machine learning algorithms to analyze traffic patterns and predict congestion, thereby enabling preemptive adjustments to alleviate bottlenecks. Additionally, the system will provide real-time updates to commuters through mobile applications and digital signage, enhancing overall traffic management and reducing commuter stress (Olu, 2024). Prior research has demonstrated that smart traffic systems can reduce congestion, lower emissions, and improve travel efficiency. By implementing this system in Jos North LGA, the study aims to assess its effectiveness in managing urban traffic and reducing delays. The research will involve simulation, field deployment, and performance evaluation to determine improvements in traffic flow, travel time, and safety. Ultimately, this project seeks to contribute to sustainable urban transportation by offering a scalable, data-driven solution to traffic congestion in resource-limited settings (Adeniyi, 2025).
Statement of the Problem :
Traffic congestion in Jos North LGA is exacerbated by outdated traffic control systems that do not adapt to real-time conditions. Traditional fixed-time signals result in inefficient traffic flow and increased travel times, leading to higher fuel consumption and environmental pollution. The lack of a dynamic system that can monitor traffic in real time prevents authorities from making timely adjustments, resulting in frequent bottlenecks and accidents. In addition, manual traffic monitoring is labor-intensive and often inaccurate, which further complicates congestion management. The absence of predictive analytics limits the ability to foresee and mitigate traffic build-ups before they occur. There is a pressing need for an IoT-based smart traffic control system that continuously collects and analyzes traffic data, enabling dynamic signal adjustments and improved traffic flow. This study addresses these issues by designing and evaluating a system that integrates sensors, machine learning, and real-time analytics to reduce congestion and enhance urban mobility. The research aims to provide a cost-effective, scalable solution for traffic management in Jos North LGA, thereby improving commuter experience and reducing the economic and environmental impacts of congestion (Ibrahim, 2023; Olu, 2024).
Objectives of the Study:
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To design an IoT-based smart traffic control system for real-time traffic management.
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To develop predictive algorithms for dynamic signal control.
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To evaluate the system’s impact on traffic flow, travel time, and safety.
Research Questions:
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How effective is the IoT-based system in reducing traffic congestion?
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What predictive models can enhance real-time traffic signal adjustments?
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How does the system improve overall urban mobility and commuter satisfaction?
Significance of the Study :
This study is significant as it develops an IoT-based smart traffic control system that addresses urban congestion through real-time data analytics and predictive modeling. The system is expected to improve traffic flow, reduce travel times, and lower emissions, thereby contributing to sustainable urban transportation. The findings will offer a replicable model for traffic management in resource-limited settings (Adeniyi, 2025).
Scope and Limitations of the Study:
The study is limited to the design, implementation, and evaluation of the smart traffic control system in Jos North LGA, Plateau State, and does not extend to rural road networks.
Definitions of Terms:
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Smart Traffic Control System: A technology-driven system that dynamically manages traffic signals using real-time data.
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Predictive Analytics: The use of statistical techniques and machine learning to forecast future events.
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Real-Time Monitoring: Continuous observation and analysis of data as it is generated.
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