🚑 Dynamic Routing System for Emergency Response in Rohingya Refugee Camps

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Project Overview

This project implements a cutting-edge dynamic routing system designed to optimize emergency healthcare and rescue operations in Rohingya refugee camps. Developed as part of SMT481 Smart City Operations Research Final Project by G1T2 AY24/25, this solution integrates advanced probabilistic models and network optimization algorithms to ensure rapid and safe emergency vehicle deployment during disasters.

Go to app/README.md for instructions on starting up the application.

Key Features

• Dynamic Routing: Adapts to real-time environmental changes and evolving emergency scenarios.
• Probabilistic Scenario Modeling: Utilizes statistical analysis and GIS techniques to predict and prepare for various emergency situations.
• Optimized Network Representation: Leverages graph theory and network analysis to model camp environments and identify critical infrastructure.
• Real-time Optimization: Implements advanced algorithms for continuous route recalibration based on changing conditions.
• Scalable Simulation Engine: Enables comprehensive scenario testing and performance evaluation.

Technical Approach

1. Data Acquisition and Preparation

   • Collects and processes geospatial data from UNHCR, OpenStreetMap, USGS, and WHO sources.
   • Cleans, aggregates, and formats data for efficient analysis.

2. Probabilistic Scenario Modeling

   • Develops statistical models to forecast flood risk zones, landslide susceptibility, and disease prevalence.
   • Implements GIS-based tools for visualizing high-risk areas and potential disruptions.

3. Network Representation and Impact Assessment

   • Constructs graph representations of camp environments using NetworkX.
   • Analyzes impact of scenarios on accessibility, population displacement, and healthcare needs.

4. Optimization Model Formulation

   • Defines objectives (minimize response time, maximize coverage) and constraints (road closures, resource limitations).
   • Implements mathematical models for emergency vehicle routing and resource allocation.

5. Algorithm Selection and Implementation

   • Employs Dijkstra's algorithm, A*, and Genetic Algorithms for optimal route finding.
   • Integrates optimization solvers for real-time decision making.

6. Simulation and Evaluation

   • Develops comprehensive simulation framework for scenario testing.
   • Implements performance metrics and sensitivity analysis tools.

7. Refinement and Deployment

   • Conducts user feedback sessions with emergency responders and community members.
   • Refines system based on evaluation results and real-world performance data.
   • Deploys system for operational use and implements continuous monitoring and improvement processes.

Implementation Details

• Primary technologies used: Python, NetworkX, optimization libraries (e.g., PuLP, CVXPY)
• Data processing: Pandas, Geopandas
• Visualization: Matplotlib, Plotly
• Simulation framework: Custom-built using Python and network analysis libraries

Future Enhancements

• Integration with IoT sensors for real-time environmental data
• Machine learning models for predictive maintenance of critical infrastructure
• Blockchain-based secure data sharing platform for emergency response coordination

License

This project is licensed under the MIT License.