Enhancing Urban Parking Efficiency Through Machine Learning Model Integration
Shaik Razia,
K. Hrudaya,
C. Archana,
D. Karishma,
C. Lakshmi,
K. Nawaz,
Dr. T. Charan Singh
An increase in vehicular traffic and a scarcity of parking spaces are creating significant challenges for
urban parking management. This study aims to tackle these issues that escalate congestion and pollution
and decrease urban productivity, by utilizing machine learning models to accurately predict parking
space availability and categorize occupancy levels. It employs a dataset from a college campus garage
collected from January 2022 to June 2023 and analyzes the performance of random forest, decision
tree, linear regression, and support vector models by comparing them, using multiple evaluation
metrics. The results revealed that the random forest model was the most reliable, as it demonstrated
strong performance in both the regression and classification analyses and was adept at estimating
the exact number of available parking spaces. A concurrent classification analysis that categorized
parking occupancy into different levels proved valuable for enhancing the quality of communication and
decision-making. An analysis of the importance of various features clearly highlighted the influence of
the day of the week on parking demand and patterns; the impact of seasonality on the volume of parking
usage; and the time of day, which plays a crucial role in determining parking behavior. The research will
benefit urban planners, facility managers, and policymakers by providing them with insights and tools
that will enhance the urban parking experience and address the complex challenges of modern urban
environments.
