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Crowd Evacuation in Stadiums Using Fire Alarm Prediction.

Afnan A Alazbah¹, Osama Rabie^1,2, Abdullah Al-Barakati¹

¹Information Systems Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)

|May 14, 2025

Summary

This summary is machine-generated.

This study introduces an AI-driven predictive fire alarm system that anticipates fire hazards before ignition using real-time sensor data. The EvacuNet model significantly improves fire detection speed and evacuation efficiency in high-density venues.

Keywords:

EvacuNet IoT-based fire detection crowd management emergency response fire alarm prediction machine learning predictive modeling stadium evacuation

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Area of Science:

Artificial Intelligence
Machine Learning
Public Safety

Background:

Traditional fire alarms are reactive, leading to delayed responses and increased panic in emergencies.
High-density environments like stadiums require advanced solutions for efficient and safe evacuations.

Purpose of the Study:

To develop and evaluate an AI-driven predictive fire alarm and evacuation model for high-occupancy venues.
To improve emergency response efficiency and public safety by anticipating fire hazards before ignition.

Main Methods:

Utilized machine learning algorithms with real-time environmental sensor data (62,630 measurements, 15 parameters).
Compared six models, including Logistic Regression, SVM, Random Forest, and the proposed EvacuNet.
Focused on early fire risk indicators like temperature, humidity, TVOC, CO2, and particulate matter.

Main Results:

EvacuNet achieved superior performance with 99.99% accuracy, 1.00 precision, and 1.00 recall.
The predictive system significantly reduced false alarms and increased fire detection speed.
AI-driven evacuation optimization minimized congestion and reduced evacuation times.

Conclusions:

AI-based predictive modeling drastically improves fire response and evacuation efficiency in large-scale venues.
Intelligent fire detection systems are essential for enhancing public safety in high-occupancy settings.
Future research should integrate IoT, reinforcement learning, and real-time crowd management for enhanced predictive accuracy.