A Literature Review of Automated Roadside Parking Monitoring Using Artificial Intelligence Algorithms

Abstract

The issue of parking has been a major concern in urban centers, primarily due to the increasing demand and daily traffic congestion. This paper endeavors to explore, process, and evaluate the existing literature on parking space detection methodologies, integrating photogrammetric techniques with deep learning models. Towards that end, various existing systems, applications, and models that have been studied were evaluated, and their impact on different test cases was assessed. The literature review was based on the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). Results indicated that smart parking systems significantly enhance dynamic parking management by leveraging deep learning techniques, particularly convolutional neural networks (CNNs). These systems process visual data from monitoring sources to generate statistics, diagrams, and maps that highlight occupied and available parking spaces, allowing for more efficient parking management and improved traffic flow. These methods contributed to improved urban mobility by providing real-time information to drivers about parking conditions along their routes. This not only enhanced convenience but also supported the development of smarter and more sustainable urban transportation solutions.