Predicting Free Parking Slot Availability Using Hybrid Machine Learning Model

Abstract

This study investigates the use of hybrid machine learning models to forecast the availability of free parking spaces in urban areas, with the goal of enhancing urban mobility. Through the application of advanced machine learning techniques, the research focuses on creating a predictive model capable of accurately identifying unoccupied parking spots in real-time. This innovative approach not only tackles the issue of limited parking availability but also contributes to mitigating traffic congestion and enhancing the overall driving experience. By conducting a thorough examination of data collection, model training, and evaluation methods, the research showcases the potential of machine learning in fostering sustainable solutions for urban transportation. The results underscore the efficacy of the hybrid model in predicting parking space availability, paving the way for future advancements in urban parking management strategies.