Paper Details

Name: Ritik Soni, Supriya Kumari & Bhawana Goyal
Country: India

Year: 2026
Volume: Volume No: 13, January, Year: 2026 (Special Issue)
Page Number: 645-653
DOI: https://doi.org/10.5281/zenodo.19107309

ABSTRACT
Rapid urbanization coupled with a rapidly growing vehicle population has led to a great concern in the management of cities’ parking structures, which have become congested leading to increased emissions and wasted time. This research will discuss the development of a Smart Parking System (SPS), integrating algorithms of Machine Learning (ML) and Internet of Things (IoT) technologies to help optimize the
management of urban parking spaces. The proposed system used IoT sensors to capture real-time data on the availability of parking spaces, vehicle occupancy, and user demand patterns. Through ML techniques such as predictive analytics and reinforcement learning, the SPS will improve decision-making processes for the
driver and the parking administrator. Case studies and sim- ulations are analyzed based on performance regarding reduced search times, utilization of spaces, and higher satisfaction for users for testing the efficacy of the system. It also talks about the integration with the existing urban infrastructure and is associated with sustainable urban mobility. Findings from the study indicated that SPS not only improved parking
efficiency but also reduced traffic congestion coupled with carbon footprints in urban areas. This study further brings forth the revolutionary capabilities of integrating ML and IoT, when merged together, to reimagine the management of parking and create smarter cities as well as more efficient ones.

Index Terms- Smart Parking System, Machine Learning, In- ternet of Things, Space Management, Urban Mobility, Predictive Analytics, Traffic Congestion, Sustainability, Real-Time Data, Vehicle Occupancy, Smart Cities, IoT Sensors.