Investigation into the Use of Quantum Computing for Real-Time Traffic Congestion Prediction in Kaduna Metropolitan Area, Kaduna State
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- NGN 5000
Background of the Study
Urban traffic congestion presents a growing challenge for rapidly expanding cities, and Kaduna Metropolitan Area is experiencing increasing difficulties in managing real-time traffic flows. Traditional traffic management systems, which rely on classical computing methods, are often hampered by the inability to process dynamic and voluminous data from traffic sensors and IoT devices quickly. This limitation leads to delayed congestion predictions and suboptimal traffic control measures (Lawal, 2023). Quantum computing, with its extraordinary processing capabilities, offers a transformative solution for real-time traffic prediction. By leveraging quantum principles such as superposition and entanglement, quantum algorithms can analyze multiple traffic scenarios concurrently, offering rapid and highly accurate predictions (Abubakar, 2024).
The integration of quantum computing into traffic management systems could significantly improve urban mobility. Enhanced real-time prediction allows for adaptive traffic signal control, effective route planning, and proactive congestion management. The potential benefits include reduced travel times, lower fuel consumption, and decreased vehicular emissions, thereby contributing to a more sustainable urban environment. Recent research indicates that quantum computing is particularly adept at solving complex optimization problems inherent in traffic prediction, such as dynamic route adjustments and network flow analysis (Chinwe, 2024). Additionally, the capability of quantum systems to process heterogeneous data sources in real time makes them ideal for urban traffic applications. Despite these advantages, the transition from classical to quantum computing in traffic management remains underexplored, and practical applications are limited (Musa, 2025).
This study intends to investigate the feasibility of employing quantum computing for real-time traffic congestion prediction in Kaduna. It will explore the integration of quantum algorithms with existing traffic systems to identify potential improvements in prediction accuracy and response time. By addressing current computational limitations, the study aims to lay the groundwork for a new generation of traffic management solutions that align with the smart city initiatives in Kaduna Metropolitan Area.
Statement of the Problem
Kaduna Metropolitan Area is grappling with severe traffic congestion, leading to economic losses, increased pollution, and deteriorating quality of urban life. Traditional traffic prediction systems, dependent on classical computing, are increasingly inadequate due to their limited ability to process large, dynamic datasets in real time (Ibrahim, 2023). This inadequacy results in delayed congestion responses and ineffective traffic management strategies. The limitations of these systems are evident in the persistent gridlock and inefficient allocation of traffic resources, which exacerbate commuter delays and elevate environmental degradation.
Quantum computing offers a promising alternative by potentially processing multiple traffic scenarios simultaneously. However, the practical implementation of quantum computing for traffic prediction remains untested in real-world settings. Challenges such as data integration, algorithmic complexity, and system compatibility with existing traffic management infrastructures pose significant obstacles (Usman, 2024). Furthermore, there is a dearth of empirical evidence supporting the use of quantum algorithms in predicting urban traffic patterns, leaving a critical gap in current research.
This study addresses these issues by investigating the application of quantum computing to enhance real-time traffic congestion prediction in Kaduna. It will identify the key technical barriers and propose a framework for integrating quantum solutions with existing systems, thereby improving prediction accuracy and enabling proactive traffic management. By bridging the gap between theoretical potential and practical application, the research aims to enhance urban mobility and reduce the negative impacts of traffic congestion (Yakubu, 2025).
Objectives of the Study
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To evaluate the potential of quantum computing for enhancing real-time traffic congestion prediction in Kaduna Metropolitan Area.
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To identify technical challenges and propose solutions for integrating quantum algorithms with current traffic management systems.
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To develop a framework for the effective application of quantum computing in real-time traffic prediction.
Research Questions
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How can quantum computing improve the accuracy and timeliness of traffic congestion predictions in Kaduna?
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What technical challenges are involved in integrating quantum algorithms with existing traffic systems?
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What framework can facilitate the effective deployment of quantum computing in real-time traffic prediction?
Significance of the Study
This study is significant as it explores the innovative application of quantum computing to address real-time traffic congestion challenges in Kaduna Metropolitan Area. Enhanced prediction accuracy and faster response times can lead to more efficient urban mobility, reduced environmental impacts, and improved quality of life. The research findings will guide policymakers and urban planners in integrating advanced quantum technologies into traffic management systems, laying the groundwork for smart city initiatives (Olayinka, 2024).
Scope and Limitations of the Study
This study is limited to investigating the use of quantum computing for real-time traffic congestion prediction in Kaduna Metropolitan Area, focusing on the stated objectives, existing traffic management systems, and selected Local Government Areas only.
Definitions of Terms
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Quantum Computing: A computational technology that utilizes quantum mechanical phenomena to process data at speeds far beyond traditional computers.
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Traffic Congestion Prediction: The forecasting of vehicular traffic conditions to manage and mitigate congestion.
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Real-Time Data Processing: The immediate processing of data as it is generated to facilitate prompt decision-making.
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