Machine Learning Strategies for Optimizing Urban Parking: A Comparative Evaluation

Parking management presents a complex challenge in urban cities, as a scarcity of parking spaces and the ever-increasing vehicular traffic have led to congestion, environmental pollution, and overall reduced urban productivity. Addressing the problem requires predicting the exact number of available parking spaces and categorizing parking occupancy levels. This study aims to achieve these tasks by employing machine learning models to accurately predict occupancy, thus optimizing parking resource allocation and enhancing the urban parking experience. A dataset derived from a college campus garage for a period spanning from January 2022 to June 2023 was used to analyze the performance of various predictive models, including random forest, decision tree, linear regression, and support vector machine. The models were compared using multiple evaluation metrics, and the results revealed that the random forest model was the most reliable. Its strong performance in regression analysis translated into precise estimations of available parking spaces. Similarly, its capability in classification analysis proved essential for categorizing parking occupancy into distinct levels, enhancing communication and streamlining decision-making processes. These findings are significant for improving parking management systems and contributing to the development of efficient and sustainable parking solutions in urban environments.