Smart Parking Solutions: A Review of Emerging Technologies and Challenges for Sustainable Smart Cities

Abstract

The rapid urbanization of cities has led to sig-
nificant challenges in transportation management, particularly
in smart parking and traffic congestion. Traditional methods
of parking space management and traffic monitoring are in-
efficient, leading to increased fuel consumption, pollution, and
driver frustration. Artificial Intelligence (AI) and Deep Learning
(DL) have emerged as powerful solutions to enhance efficiency,
optimize resource allocation, and improve real-time monitor-
ing. These intelligent systems utilize Internet of Things (IoT)
sensors, convolutional neural networks (CNNs), reinforcement
learning, and computer vision techniques to automate parking
detection, traffic flow prediction, and congestion management.
AI-powered smart parking systems provide real-time insights into
parking availability, reducing search times and optimizing space
utilization. Similarly, deep learning-based traffic management
techniques enable predictive analytics for congestion control,
anomaly detection, and automated traffic signal adjustments.
Various AI-based models have demonstrated high accuracy in
vehicle detection, parking space prediction, and real-time traffic
analysis. However, challenges such as high computational costs,
data privacy concerns, scalability issues, and real-time imple-
mentation constraints still hinder widespread adoption. This
paper reviews recent advancements in AI-driven smart parking
systems, deep learning-based vehicle detection, and intelligent
transportation systems. A detailed analysis of existing literature
is provided, highlighting key technologies, methodologies, and
their effectiveness in real-world applications. Furthermore, the
paper discusses the limitations of current approaches and iden-
tifies areas requiring further research. Finally, future research
directions are discussed to improve the accuracy, efficiency,
and scalability of AI-based intelligent transportation systems,
ensuring sustainable and smarter urban mobility solutions.