Cloud-Orchestrated Automated Parking Platform with Real-Time User Notification and Resource Optimization

Abstract

The current parking infrastructures are under unprecedented strain due to the rapid growth of urban mobility. Due to their heavy reliance on human intervention and lack of real-time monitoring, conventional parking techniques cause traffic jams, inefficient use of available space, and user annoyance. In order to facilitate smooth, real-time parking operations, this paper proposes a Cloud-Based Automated Smart Parking Management System that makes use of cloud orchestration and Automatic Number Plate Recognition (ANPR). In order to automatically manage vehicle identification and parking length, the suggested system takes pictures of license plates at entry and exit points, processes them using an ANPR pipeline, and logs timestamps. Users receive real-time messages about entry confirmation, slot assignment, and leave events from a cloud-based backend that also stores consolidated parking data and dynamically assigns parking spaces based on availability. The solution improves operating efficiency and scalability by doing away with physical tokens and manual oversight. The cloud-centric architecture guarantees dependable data processing, effective use of resources, and the capacity to manage several parking events at once. The suggested system, which offers enhanced user experience, shorter parking search times, and optimal parking space usage, is appropriate for implementation in large-scale settings including retail centers, corporate campuses, and smart city infrastructures

Introduction

The text presents a Cloud-Based Automated Smart Parking Management System designed to address growing parking challenges caused by urbanization and increased vehicle ownership. Traditional parking systems rely on manual processes or static allocation methods, leading to congestion, inefficient space usage, fuel waste, and poor user experience. While existing smart parking solutions use IoT and automation, many suffer from scalability limitations, high infrastructure costs, localized processing, and insufficient real-time user interaction.

To overcome these issues, the proposed system integrates Automatic Number Plate Recognition (ANPR) with cloud-based infrastructure to enable fully automated, scalable, and efficient parking management. ANPR allows contactless vehicle identification without physical tags, while cloud computing provides centralized data management, real-time processing, scalability, and fault tolerance. The system automatically records vehicle entry and exit, dynamically assigns parking slots based on availability, tracks parking duration, and sends real-time notifications to users.

A layered architecture is adopted to ensure modularity and ease of deployment. Key layers include sensing (image capture at entry/exit points), edge processing (local preprocessing to reduce latency), cloud storage, AI-based ANPR recognition, application logic, database management, slot management, notification services, and user/administration interfaces. This structured design enables efficient coordination of parking operations and supports large-scale environments such as shopping malls, corporate campuses, and smart cities.

The workflow automates the entire parking process—from vehicle arrival and license plate capture to slot allocation, duration calculation, exit processing, and user notification—while minimizing human intervention. Overall, the proposed cloud-orchestrated ANPR-based system offers a scalable, cost-effective, and user-centric solution that improves parking space utilization, reduces congestion, and enhances operational efficiency for modern smart city deployments.

Conclusion

By combining real-time car recognition, automatic slot allocation, and cloud-based monitoring, the Cloud-Orchestrated Smart Parking Management System shows a notable improvement over conventional parking techniques. By utilizing AWS cloud services and Automatic Number Plate Recognition (ANPR), the system enhances total parking space utilization, lowers waiting times, and offers high vehicle identification accuracy. The system\'s automation reduces human interaction, improves user convenience, and helps to lessen traffic jams and vehicle emissions in parking lots. The results obtained from the system show that automation and cloud integration not only simplifies parking operations but also offer useful insights via data analytics, facilitating improved urban infrastructure management and planning.

References

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Copyright

Copyright © 2026 A. G. Vignesh Sivaram, J. Harish, Mrs. Santhana Arumuga Sankari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.