Facility Evacuation Prediction Using Historical Occupant Movements
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Solution Overview
Problem
Traditional emergency evacuation planning and training methods lack realism and fail to incorporate critical access control data, leading to inefficient evacuation routes and increased evacuation times, potentially risking lives.
Innovation Solution
A system that leverages access control data to model occupant behavior and generate evacuation routes, using AI to predict evacuation times and optimize routes based on historical movement patterns, with feedback from drills to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional static evacuation plans and live drills are used, then evacuation procedures can be established and trained, but the plans lack realism and do not incorporate access control data, resulting in suboptimal evacuation routes and longer evacuation times
Solution Approach 1:
The system performs preliminary analysis of historical access control data and occupancy patterns before emergencies occur to pre-determine optimized evacuation routes. This allows the system to have pre-calculated, data-driven evacuation paths ready when emergencies happen, eliminating the need for suboptimal static plans while reducing actual evacuation time.
Solution Approach 2:
The system continuously collects access control data and occupancy information during normal operations, analyzes this feedback to identify patterns, and uses this information to dynamically optimize evacuation routes. This feedback loop ensures evacuation plans remain accurate and up-to-date without requiring frequent disruptive live drills.
2Reliability
If frequent live drills are conducted, then employee safety training is improved, but work routines are disrupted and employee confidence erodes
Solution Approach 1:
The system creates a virtual digital twin of the facility that replicates real-world occupancy patterns and traffic flows. Evacuation training and optimization can be conducted in this virtual environment using historical data, providing realistic training scenarios without disrupting actual work routines or requiring employees to leave their posts.
Solution Approach 2:
The system replaces physical live drills with a data-driven virtual simulation system that uses access control data to model realistic evacuation scenarios. This substitution maintains training effectiveness while eliminating the productivity loss and disruption associated with frequent physical drills.
3Loss of time
If evacuation routes are optimized based on historical movement patterns, then evacuation time is reduced, but the system complexity increases due to data collection and analysis requirements
Solution Approach 1:
The system leverages the existing access control infrastructure for multiple purposes: it continues to provide security access control while simultaneously collecting data for evacuation route optimization. This multi-functionality reduces overall system complexity by reusing existing hardware and data collection mechanisms rather than adding separate dedicated systems.
Solution Approach 2:
The system automatically collects, analyzes, and processes access control data to generate optimized evacuation routes without requiring manual intervention. The system self-updates its models and recalculates routes based on new data, reducing operational complexity and eliminating the need for manual system management while maintaining reduced evacuation times.
Data Source
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AI summary
Emergency evacuation times may be predicted for a facility. Location information may be captured for each of a plurality of people within the facility and one or more historical movement patterns of one or more of the plurality of people associated with the facility may be identified based at least in part on the captured location information. One or more predefined evacuation routes may be identified for the facility. The emergency evacuation time for evacuating the facility using the one or more predefined evacuation routes in response to an evacuation event may be predicted based at least in part on the one or more historical movement patterns of one or more of the plurality of people associated with the facility.