IoT-Driven Pull Scheduling to Avoid Congestion in Human Emergency Evacuation

Area of Science:

• Emergency management
• Internet of Things (IoT) applications
• Human behavior in emergencies

Background:

• Efficient human evacuation is crucial during emergencies, particularly in confined spaces like cruise ships.
• Congestion in passageways significantly complicates and slows down evacuation processes.
• The Internet of Things (IoT) offers potential solutions for real-time monitoring and dynamic guidance.

Purpose of the Study:

• To analyze an emergency evacuation system using an analytical queueing network approach.
• To propose and evaluate the IoT-based Pull Policy for reducing downstream congestion during evacuations.
• To demonstrate the effectiveness of the Pull Policy in a realistic cruise ship evacuation scenario.

Main Methods:

• Utilized an analytical queueing network model to represent the evacuation system.
• Implemented the IoT-based Pull Policy, which directs evacuees based on downstream conditions.
• Simulated evacuation scenarios on a model of a commercial cruise ship.

Main Results:

• The IoT-based Pull Policy effectively reduces downstream congestion by signaling evacuees.
• The queueing network model allowed for efficient comparison of various routing rules and speed parameters.
• Numerical examples validated the policy's value for timely evacuation in confined spaces.

Conclusions:

• The IoT-based Pull Policy is a valuable strategy for enhancing the efficiency and safety of emergency evacuations.
• Dynamic guidance systems leveraging IoT can significantly mitigate congestion in complex environments.
• This approach is particularly effective for large-scale evacuations from vessels like cruise ships.