Graph-Based Emergency Routing System

Made by Viren Singh

Hub Incident Selected route
Priority City --
Risk Weight --
Stress Mode --
1
Input

Hazard severity, affected people, rescue hubs, incidents, and selected route stress.

2
Process

Build a weighted graph where stress increases travel cost and reduces route capacity.

3
Output

Rank feasible paths by risk-aware cost and show the safest first dispatch route.

System Status Backend ready. Click Run Decision.

Live data sources will appear here after the optimizer runs.

Weighted graph routing Ready

Route Ranking

0 nodes - 0 edges

Active Incidents

0 incidents
Incident City People Severity Risk weight

Evaluation: Baseline vs ResQNet

--
Metric Without system With ResQNet

Scaling + Stress Test

Not run
Research Model

Graph-Based Emergency Routing System with Risk-Aware Path Optimization

Graph model Hubs and incidents are nodes. A directed edge exists when a hub can reach an incident inside the regional route limit.
Risk-weighted routing Each edge cost combines travel time, severity, demand load, route stress, and hazard type. Higher stress directly increases route cost.
Capacity allocation Edmonds-Karp max-flow sends limited rescue capacity through feasible hub-to-incident edges.
Decision output The first route is the lowest-risk feasible allocation after stress and capacity are applied, not just the shortest visual line.