Current Volume 9
Abstract
Public-service queue systems in hospitals, banks, and citizen-service centers still face long waits, weak transparency, and inefficient use of counters. Recent studies show that machine learning and lightweight sensing can improve waiting-time estimation and queue visibility, but the most directly relevant evidence is concentrated in healthcare, banking, and service-center applications. This paper reviews ten highly relevant queue studies and uses them to propose an ML-based smart queue management framework for public-service delivery. The review identifies queue length, arrival rate, service duration, and real-time queue-state signals as the most useful predictive inputs, while the main unresolved gaps are cross-domain generalization, prediction-allocation integration, infrastructure-light deployment, and balanced evaluation of service outcomes. Based on these findings, the paper proposes a framework that combines event capture, preprocessing, feature engineering, waiting-time prediction, dynamic counter allocation, monitoring, and HOT-Fit evaluation. The framework is intended to improve transparency, reduce congestion, and support practical queue modernization in public-service settings.
Keywords
Smart Queue Management System, Waiting-Time Prediction, Machine Learning, Dynamic Counter Allocation, Public Service Delivery, HOT-Fit Evaluation
Citations
IRE Journals:
Dr. Balamurugan S, Dhanush S "ML-Based Smart Queue Management System for Public Services" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2662-2673 https://doi.org/10.64388/IREV9I11-1717976
IEEE:
Dr. Balamurugan S, Dhanush S
"ML-Based Smart Queue Management System for Public Services" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717976
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12