Year: 2025
Type: article
Abstract: 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.
Institution: K.S. Rangasamy College of Technology
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410370989