An Investigation of AI-Based Traffic Flow Prediction for University Campus Transport Systems: A Case Study of Federal Polytechnic, Bauchi (Bauchi LGA, Bauchi State)
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- NGN 5000
Background of the Study
University campuses often face significant traffic congestion, particularly during peak hours when students and staff rush to attend lectures or other academic activities. Inefficient traffic management can lead to delays, frustration, and even safety concerns. Traditional traffic management techniques, such as manual traffic monitoring and fixed schedules, often fail to adapt to dynamic traffic patterns on campuses (Odeyemi & Owolabi, 2023). AI-based traffic flow prediction models offer a promising solution by leveraging machine learning and real-time data to forecast traffic conditions and optimize campus transport systems. These models use historical traffic data, environmental factors, and real-time inputs (e.g., weather, events) to predict traffic flow and help in better route planning, reducing congestion and improving the overall transport experience (Adewale & Ibrahim, 2024).
At Federal Polytechnic, Bauchi, located in Bauchi LGA, Bauchi State, the university faces challenges with traffic flow on its campus, particularly in high-traffic areas like lecture halls, parking lots, and event spaces. This study aims to investigate the role of AI-based traffic flow prediction models in improving campus transportation efficiency. The research will assess the effectiveness of these models in predicting traffic patterns and optimizing transport routes to reduce congestion and enhance student and staff mobility.
Statement of the Problem
The Federal Polytechnic, Bauchi, has struggled with traffic congestion, particularly during class changes and university events, which leads to delays and safety concerns. Traditional traffic management methods are inadequate for dynamically adjusting to the variable traffic patterns on campus. There is a need for a more sophisticated, real-time solution that can predict and manage traffic flow effectively. AI-based traffic flow prediction models offer a potential solution but have not yet been fully explored in the context of Nigerian university campuses.
Objectives of the Study
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To investigate the feasibility of using AI-based traffic flow prediction models for campus transport systems at Federal Polytechnic, Bauchi.
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To evaluate the effectiveness of AI-based traffic prediction models in optimizing traffic flow and reducing congestion on the campus.
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To assess the impact of AI-based traffic flow prediction on the overall transportation experience for students and staff.
Research Questions
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How feasible is the implementation of AI-based traffic flow prediction models at Federal Polytechnic, Bauchi?
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How effective are AI-based traffic prediction models in optimizing campus traffic flow and reducing congestion?
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What is the impact of AI-based traffic flow prediction on the transportation experience of students and staff?
Significance of the Study
The study will provide insights into the potential of AI-driven traffic management systems in enhancing campus transport systems. The findings could guide the adoption of AI-based solutions for traffic flow optimization in universities, leading to smoother campus mobility and improved safety for students and staff.
Scope and Limitations of the Study
The research will focus on investigating AI-based traffic flow prediction models for campus transport systems at Federal Polytechnic, Bauchi, located in Bauchi LGA, Bauchi State. The study will examine the feasibility and effectiveness of these models, excluding other transport management issues and external factors affecting traffic flow.
Definitions of Terms
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AI-Based Traffic Flow Prediction: The use of artificial intelligence models to predict traffic patterns based on historical data and real-time inputs to optimize traffic management.
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Campus Transport System: The network of vehicles, routes, and infrastructure that facilitates the movement of students, staff, and visitors within a university campus.
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Machine Learning: A subset of AI that uses algorithms to analyze data, identify patterns, and make predictions or decisions without explicit programming.
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