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🚑 Dynamic Routing System for Emergency Response in Rohingya Refugee Camps

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.