A human‑centric transparent queue management system for public service offices
Q-Vue is a web‑based queue management system designed for public service offices to reduce uncertainty, confusion, and crowding during long waits. Instead of only displaying token numbers, the system focuses on clear communication, fairness, and decision support, helping visitors understand: how long they might wait, whether they should stay or return later, and what is happening in the queue right now.
This project was built as part of Code.INIT() 2026 by a pre‑beginner team, using simple and reliable technologies.
In public offices: Visitors wait without knowing how long it will take. They are unsure if they are in the correct queue. Staff are repeatedly interrupted for status updates. Existing queue systems lack clarity and transparency. This results in anxiety, inefficiency, and crowding, even when service itself is fair.
Q-Vue provides a transparent, fair, and human‑friendly queue system that: explains what is happening, communicates uncertainty honestly, and helps visitors decide whether to wait or return later. The innovation lies not in complex technology, but in clear communication and thoughtful design.
Visitor‑Side Features: 1)Task‑based ticket selection (Quick Approval, Document Review, Consultation, Complex Filing) 2)Token generation & live position tracking 3)Estimated waiting time 4)Confidence Indicator(Friendly text that becomes more positive as the visitor approaches their turn) 5)Return Advisor with Safe Window(Suggests when a visitor can safely step away and return without losing their place) 6)Queue Predictability Indicator(Shows whether the queue is stable or may have delays) 7)Now Serving (per counter) 8)Next tokens in line 9)Live multi‑counter view 10)Clear visual separation of counters and queues
Staff Dashboard Features: 1)Multiple active counters 2)Call Next Token 3)Mark Service Completed 4)Skipped token handling
Clear, no‑skip serving logic to ensure fairness
Reason for Delay messages (e.g., long consultations, high load) Fairness statement Tokens are served strictly based on queue order and task type End‑of‑Service message Polite closure after completion
Frontend: HTML, CSS, JavaScript Backend: Python (Flask) Data Handling: In‑memory queues (prototype‑friendly) UI Design: Google Stitch (for rapid, clean UI design)
->Conclusion Transparent Queue demonstrates that impactful systems do not need heavy technology — they need clear thinking, fairness, and empathy for users.