Development of ICT-Based Crowd-Dense Public Data Utilization Management

Overview

Hyper-scale events in urban areas, stadiums, airports, and public transportation transfer hubs experience sudden crowd surges, significantly increasing the risk of accidents. Existing crowd management methods rely on CCTV monitoring and manual staffing, making real-time response difficult and limiting the ability to take swift action when accidents occur. Accordingly, this technology aims to develop a smart management system that analyzes crowd density using public data and AI technology, enabling rapid response when risks arise.

Project description

This system analyzes real-time data in crowded public spaces to prevent accidents and support efficient personnel deployment.

Using AI algorithms, it monitors crowd movements in real time and predicts density levels in specific areas. By accessing large events or periods of high public transportation usage, the system helps identify potential danger zones in advance and enables the establishment of response plans. It gathers real-time data from a variety of IoT devices, including CCTV, smart sensors, drones, and mobile devices, enabling the evaluation of crowd speed, direction, and congestion to maintain smooth flow.

When crowd density exceeds a certain threshold or abnormal behavior patterns are detected, immediate alerts are triggered, prompting rapid response through coordination with relevant agencies. It also supports real-time adjustment of crowd management strategies in collaboration with transportation and security authorities.

Key technological features

• AI-Based Crowd Density Analysis System: Analyzes video and sensor data to monitor crowd movement patterns in real time
• IoT-Based Real-Time Data Collection: Collects and analyzes real-time data from CCTV, smart sensors, and mobile devices in public spaces
• Risk Detection and Response System: Provides automatic alerts and response plans when crowd density exceeds set thresholds or abnormal behavior is detected

Expected outcomes

This technology enables accident prevention and rapid response in public spaces where large crowds gather, maximizing crowd management efficiency. Furthermore, integration with public safety management systems allows for the establishment of a more systematic crowd control system.

Launch year: 2024

Attribution

Case study courtesy of the National Information Society Agency, Republic of Korea, Apolitical's Content Partner. Sourced from Use Cases of Public AI Service, Vol.1,  https://eng.nia.or.kr/site/nia_eng/ex/bbs/View.do?cbIdx=31975&bcIdx=28997&parentSeq=29038