SmartQueue: AI-Based Real-Time Queue Prediction and Workforce Optimization скачать в хорошем качестве

SmartQueue: AI-Based Real-Time Queue Prediction and Workforce Optimization

SmartQueue is an AI-powered system designed to improve queue management and service efficiency in high-footfall environments such as metro stations, hospitals, banks, airports, and government offices. The system uses computer vision and machine learning to monitor queue lengths in real time, predict future congestion, and support intelligent workforce allocation. Using a camera feed, the system detects and counts people in queues through the YOLO object detection model. The collected data is analyzed to understand crowd patterns, while a predictive model (LSTM) forecasts future queue loads. Based on these insights, optimization techniques can be applied to recommend efficient staff distribution across service counters. The goal of SmartQueue is to reduce waiting times, improve operational efficiency, and support data-driven decision-making in smart infrastructure and public service environments. Key Features: • Real-time queue detection using AI • Crowd monitoring through computer vision • Predictive queue analytics using machine learning • Scalable architecture for smart city applications • Interactive dashboard for monitoring system performance This project demonstrates how AI and data analytics can transform traditional queue management into an intelligent and proactive system for modern urban environments.