Evacuation Path Planning System Scalability Optimization
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Solution Overview
Problem
Current methods for planning evacuation paths, such as Linear Programming and Network Flow approaches, face scalability issues, fail to consider distance to exits, and do not account for probabilistic human behavior, leading to suboptimal evacuation times and inefficient path utilization.
Innovation Solution
The system iteratively identifies shortest paths from sources to sinks in increasing order of transit time, computes combined evacuation time, and redistributes evacuees based on a probabilistic behavioral model, ensuring consistent path usage and minimizing expected evacuation time by considering both shortest and suggested paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If Linear Programming based polynomial time techniques are used for evacuation planning, then the evacuation paths can be computed in polynomial time, but the time complexity makes it non-scalable even for mid-sized networks
Solution Approach 1:
The network is segmented into multiple sub-networks or regions, and evacuation paths are computed separately for each segment. This divides the large-scale optimization problem into smaller, more manageable sub-problems that can be solved independently and then combined, improving both computational efficiency and scalability while maintaining polynomial time complexity.
Solution Approach 2:
The patent introduces a hierarchical dimension to the evacuation planning by implementing multi-level path computation. Instead of solving the entire network at once, the system operates at multiple levels (local, regional, global), allowing the solution to scale by adding vertical layers of computation rather than expanding horizontal network size linearly.
2Ease of operation
If Capacity Constrained Route Planner (CCRP) techniques are used to find shortest paths, then paths can be found from any source to any sink, but space complexity and unnecessary expansion of source nodes in each iteration are main disadvantages
Solution Approach 1:
The patent extracts and eliminates the unnecessary expansion of source nodes from the CCRP algorithm. By identifying and removing this redundant operation, the algorithm maintains its ability to find shortest paths while significantly reducing space complexity and avoiding unnecessary computational overhead in each iteration.
Solution Approach 2:
Instead of expanding all source nodes fully in each iteration as CCRP does, the patent applies partial expansion only where necessary for finding optimal paths. This selective approach reduces the computational burden and space requirements while still achieving complete path finding capability across the network.
3Speed
If CCRP++ is used to run faster than CCRP, then computation speed improves, but the quality of solution is not good because availability along a path may change between the times when paths are reserved and when they are actually used
Solution Approach 1:
The patent performs preliminary actions by reserving path availability and capacities before actual evacuation occurs. The system pre-computes and locks in the availability of network resources along selected paths, ensuring that when evacuees actually use these paths, the conditions remain as planned. This preliminary reservation mechanism maintains solution quality while preserving computational speed.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor changes in path availability and capacity between reservation and actual use. When changes are detected, the system adjusts subsequent path selections to account for these changes, ensuring solution quality is maintained despite dynamic network conditions while keeping computation efficient through targeted rather than exhaustive re-evaluation.
4Productivity
If minimum cost transshipment approach is used, then evacuation paths can be computed efficiently, but it does not consider the distances between evacuees and exits and may fail if there are exits very far away
Solution Approach 1:
The patent changes the parameters used in the transshipment approach by incorporating distance metrics into the cost function. Instead of using only capacity and flow parameters, the system integrates distance-based parameters that penalize paths leading to distant exits, ensuring that evacuation routes are not only computationally efficient but also physically reasonable and accessible to evacuees.
5Reliability
If earliest arrival approach is used, then optimal flow over time is achieved and problems with distant exits are avoided, but the exit assignment computed may not be optimal
Solution Approach 1:
The patent introduces dynamics into the exit assignment process by making assignments adaptive rather than static. The system continuously evaluates and adjusts exit assignments based on real-time network conditions, evacuee locations, and path availabilities. This dynamic reassignment mechanism ensures that while maintaining optimal flow over time, the system can also optimize evacuation time by redirecting evacuees to better exits as conditions evolve during the evacuation process.
Data Source
AI summary
Methods and systems for planning evacuation paths are provided to identify an optimum path for evacuation of every evacuee from a region of interest. Methods of the instant disclosure ensure that for each suggested evacuation path, the capacity of any edge on the path is not exceeded and the evacuation time is minimum. A path once identified is maintained. A randomized behavior model is employed to re-distribute evacuees in emergency situations. This provides optimum evacuation time that employs an improved technique with optimized run time and evacuation time after taking into consideration herd behavior.