What it does
It lets users join queues virtually, view real-time position and AI-predicted wait time, and get alerts when it’s their turn and allows service providers to manage queues helping businesses reduce crowding and customers avoid long, uncertain waits.
Your inspiration
The inspiration came from our personal experiences of waiting endlessly at clinics, service centers, and shops without knowing how long it would take for our turn. These restless and frustrating waits made us realize how common this problem is. We noticed that in many places, people still stand in physical queues with no clear idea of their position or the remaining wait time. This led us to the idea of creating a system that brings transparency and efficiency to queue management. Our goal was to help users save time and reduce stress, while also helping businesses manage queues more effectively, avoid crowding, and improve customer service.
How it works
Our system is a smart web-based queue management tool that lets users join a queue virtually from their device. After signing up and logging in, users can take a digital token to join the queue. They instantly see their position in the line and an estimated wait time, which is calculated using a machine learning model Random Forest. This model is initially trained on synthetic data that includes information like day, time, position, and past wait times, and updates regularly as new data is added. Admins have a separate login and dashboard where they can add users manually with their name, email, and order details. They can also mark users as “Served,” edit their details, or remove them from the queue. All data, including login info and queue details, is stored securely in Firebase. Users can only log in after signing up, and admins are pre-approved through Firebase. We're also adding a notification feature to alert users via email when it’s almost their turn.
Design process
1. Conceptualization: We outlined the key features: user signup/login, virtual token generation, real-time queue display, admin control panel, and AI-based wait time prediction. 2. Prototyping (UI/UX): We sketched basic wireframes for a clean, user-friendly interface including landing page, user dashboard, and admin dashboard. 3. Frontend Development: Using ReactJS and VanillaCSS, we built separate pages for user signup/login and admin login, and dashboards for both. Each login includes proper authentication and redirection. 4. Backend Integration: We used Firebase Realtime Database and Authentication to store user data securely, manage tokens, and control admin access. 5. ML Integration: We created a Random Forest model trained on synthetic data which is updated with daily real data inputs representing queue patterns (day, time, position, wait time) to predict future wait times. 6. Testing and Iteration: We tested functionalities like token creation, position updates, admin controls, and ML predictions. We added filters (Today, This Week, All) for viewing daily serves and queue management options for admins. 7. Upcoming Improvements: We're now adding email notifications to alert users before their turn and improving the accuracy of predictions using real-time data.
How it is different
Our system stands out by combining real-time virtual queue management with AI-powered wait time prediction. Unlike basic token systems or manual queues, our platform uses a Random Forest machine learning model to estimate each user's wait time based on position, time, and day. This gives users a clear idea of how long they’ll wait, reducing anxiety and improving planning. What also makes it unique is the dual control system—both users and admins can generate tokens. Admins have full control to add, edit, serve, or remove users, along with filters to track daily and weekly served counts. All data is synced live using Firebase, and only verified users and pre-approved admins can log in. We also plan to add smart notifications, alerting users when their turn is near—something missing in most queue systems. Our design is not just user-focused but also optimized for businesses to manage crowd flow and improve service efficiency.
Future plans
We plan to introduce email notifications so users get timely alerts as their turn approaches, enhancing their overall experience. To make predictions more accurate, we’ll expand our dataset with new attributes like service type, estimated service time, crowd density, and time-based patterns. A fully mobile-responsive version is underway to ensure smooth usage across all devices. Long-term, we aim to deploy this system in real-world settings like clinics, salons, and service centers to reduce crowding, streamline queue management, and improve operational efficiency for businesses and convenience for users.
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