01 · RESEARCH PRESENTATION
SMART QUEUE
MANAGEMENT
SYSTEM
Machine Learning-Based Solution to Service Delivery Challenges in South Africa
Peace Khutso Molimo
Supervisor: Dr Ayinde Mohammed Usman
University of the Western Cape · 2026
Department of Computer Science
01
02 · PROBLEM STATEMENT
THE QUEUE
CRISIS
5+ HOURS
Average wait time at Department of Home Affairs
63.1M
Population in 2025 — projected to exceed 65M in 2026, increasing demand
POPIA
Compliance required — sensitive citizen data must be protected
Peace Khutso Molimo · UWC · 2026
03 · RESEARCH AIM & SCOPE
RESEARCH
AIM
To design a machine learning-based Smart Queue Management System that addresses inefficient queue management in South African public service environments through real-time monitoring and waiting time prediction.
SPECIFIC GAPS ADDRESSED
GAP 01
Contextual Application
Limited queue management research in South African government institutions (DHA) where walk-ins & unpredictable arrivals are common.
GAP 02
Predictive Integration
Few systems combine real-time queue monitoring with ML-based waiting time prediction to proactively optimize service flow.
Peace Khutso Molimo · UWC · 2026
04 · LITERATURE REVIEW
KEY THEMES
01
Queue Management
Digital solutions in hospitals & government offices reduce wait times significantly. DEA models and real-time updates prove effective [1],[2],[3].
02
Predictive Techniques
ML algorithms (Random Forest, ANN, Gradient Boosting) outperform traditional M/M/s and FCFS models in complex, multi-arrival systems [4],[5],[6].
03
Multi-class Systems
Multi-class queue models outperform single-class models by 30–40% accuracy when combined with regression trees and snapshot predictors [5].
04
Cybersecurity
Public service systems in SA face ransomware, phishing & data breaches. RBAC, MFA, and staff training are key recommended mitigations [7],[8],[9].
Peace Khutso Molimo · UWC · 2026
05 · PROPOSED SYSTEM
THE SYSTEM
OVERVIEW
CUSTOMER
Join queue via app, web or kiosk. Track position, receive notifications, manage bookings.
STAFF
View live queue, call next customer, update service status, handle priority cases.
ADMINISTRATOR
Monitor system performance, manage users, view analytics, configure settings.
ACCESS CHANNELS
Mobile App
Web Portal
On-site Kiosk
Peace Khutso Molimo · UWC · 2026
06 · USER JOURNEY
HOW IT WORKS
1
Join Queue
Customer uses app, web or kiosk to register and select service type.
2
Position Assigned
System assigns queue number and records arrival time and service type.
3
ML Predicts
KNN, Random Forest & ANN models predict wait time and serving time in real time.
4
Notification
Customer is alerted when their turn approaches. Staff calls them to the counter.
5
Admin Monitors
Administrator views live dashboard queue load, staff activity and performance.
Peace Khutso Molimo · UWC · 2026
07 · SYSTEM ARCHITECTURE
MULTI-LAYER
ARCHITECTURE
USER INTERFACE LAYER
Mobile App · Web Portal · On-site Kiosk
APPLICATION LAYER
Queue Logic · Booking Management · Notifications
MACHINE LEARNING LAYER
KNN · Random Forest · Artificial Neural Networks
DATA LAYER
User Records · Queue History · Real-time State
SECURITY LAYER
RBAC · Multi-Factor Authentication · Activity Logging
Each layer is interconnected
The ML layer is the core; it learns from real-time and historical queue data to predict both
waiting time and serving time.
The security layer wraps the entire system ensuring POPIA compliance and data protection.
Peace Khutso Molimo · UWC · 2026
Figure 1: Proposed System Architecture
08 · REQUIREMENTS
SYSTEM
REQUIREMENTS
FUNCTIONAL
NON-FUNCTIONAL
Performance
Real-time updates with minimal delay
Security
Auth, access control, POPIA compliance
Usability
Simple for all digital literacy levels
Reliability
Consistent operation, graceful error handling
Scalability
Handles peak periods without degradation
Peace Khutso Molimo · UWC · 2026
9·CONCLUSION
WHAT WAS
ACHIEVED
NEXT STEPS
→
System implementation
→
Dataset collection & model training
→
Evaluation & testing
→
User acceptance testing
THANK YOU
4352859@myuwc.ac.za · University of the Western Cape
10 ·REFERENCES
[1] A. Yalçıner, E. Gökalp, and A. Dikici, "An Optimized Queue Management System to Improve Efficiency in Public Hospitals," International Journal of Health Care Quality Assurance, vol. 33, no. 7–8, pp. 477–495, 2022.
[5] QTech Queueing System, "Clinic Queue Management System: Real-Time Queue Updates," 2022. [Online]. Available: https://qtechqueueingsystem.com/clinic-queue-management-system/
[2] J. Tan, "Implementation of a QR-Based Clinic Queue Management System," Journal of Medical Informatics, 2025, e77297.
[3] I. Cohen, "Delay Prediction for Managing Multiclass Service Systems: An Investigation of Queueing Theory and Machine Learning Approaches," 2022. [Online]. Available: https://izackcohen.com/wp-content/uploads/2022/12/Delay_Prediction_for_Managing_Multiclass_Service_Systems_An_Investigation_of_Queueing_Theory_and_Machine_Learning_Approaches.pdf
[4] C. Xu et al., "Prediction of Waiting Times in Multi-Class Service Systems," Computers & Industrial Engineering, 2015.
[5] F. Rossi et al., "Real-Time Prediction of Waiting Times in Emergency Departments Using Machine Learning," Decision Support Systems, 2021.
[6] African Journal of Information and Communication (AJIC), "Cyber Threat Landscape in South Africa," 2023. [Online]. Available: https://ajic.wits.ac.za/article/download/12938/17798/76846
[7] BMC Medical Education, "Cybersecurity Awareness in African Healthcare Systems," 2025. [Online]. Available: https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-025-07755-x
[8] Devdiscourse, "Why South Africa's Cybersecurity Policies Struggle Without Public Trust," 2024. [Online]. Available: https://www.devdiscourse.com/article/technology/3731429-why-south-africas-cybersecurity-policies-struggle-without-public-trust