DISTRIBUTED INTELLIGENCE FOR CAMPUS PARKING ALLOCATION AND TRAFFIC OPTIMIZATION
DOI:
https://doi.org/10.63458/ijerst.v3i1.106Keywords:
Campus parking, Machine learning, Real-time data, Sensors, Cameras, Parking management, Decision trees, SVMs, Neural networks, Traffic congestion, Smart mobilityAbstract
Efficient campus parking management is needed to minimise congestion, wastage of resources, and delays caused to students, staff, and visitors. The study focused on applying machine learning techniques to improve parking management systems using real-time data analysis. Such ML techniques comprise decision trees, SVMs, and neural networks developed through cameras and sensors for predicting parking availability, optimising resource allocation for multiple locations, and directing vehicles to them. The system dynamically adapts to varying traffic conditions, load levels, and periods of the day to allow further optimisation of space usage and improved mobility. Besides other advantages, predictive analytics endorses strategic infrastructure planning and improves operational efficiency. In essence, the findings of the study prove that the ML solutions for parking will enhance both campus mobility and traffic congestion during peak hours, thus encouraging sustainability.
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