Emergency Routing Project

Overview

The Emergency Routing Project focuses on optimizing medical emergency response in Obio-Akpor, Rivers State.
The goal is to identify the closest hospital for any emergency, taking into account:

• Distance
• Travel time
• Hospital capacity
• Service rating

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Tools and Libraries

• Python: main programming environment
• Pandas / NumPy: data handling and computation
• GeoPandas / Shapely / Fiona: geospatial manipulation
• OSMNx / NetworkX: road networks, graph modeling, shortest paths
• SciPy: optimization routines
• Kepler.gl / Matplotlib / Plotly: geospatial visualization
• Google Colab: coding & collaboration
• Git / GitHub: version control

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Data Notes

• Hospitals dataset manually collected into CSV: Name, coordinates, service rating.
  • Reason: OpenStreetMaps does not have a lot of hospitals plotted onto its maps.
• Road network extracted via OSMNx for Obio-Akpor polygon.
• Data integrity maintained: no missing values, clean and ready for modeling.

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Project Phases

Phase 1 Completed

• Extract Obio-Akpor boundaries.
• Load hospital CSV and road network.
• Convert road network to GeoDataFrame.
• Prepare graph base for routing.
• Integrate hospitals with the road network graph.
• Hospitals plotted on map with lon/lat.

Phase 2 In Progress

• Emergency Route Optimization.
• Compute one-to-many shortest paths using Dijkstra’s algorithm.
• Compute route metrics: distance, travel time (road-type speeds), hospital capacity, service rating.
• Visualization: highlight closest hospital, color-code alternatives.

Phase 3 Planned

• Simulation of emergency scenarios.
• Interactive dashboards (Tableau).
• Documentation and reporting of results.

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Special Considerations

• Travel time calculated using average speeds by road type:
  • Highway → 80 km/h
  • Primary → 60 km/h
  • Minor → 40 km/h
• Multi-criteria cost function considers distance, travel time, hospital capacity, and rating.
• Workflow maintained in Google Colab.
• GitHub used for version snapshots (token-based authentication).

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Collaboration & Version Control

• Shared Google Drive folder for Colab notebooks & resources.
• Folder structure:
  /Project Code, Resources and Implementation/
  • Route_Optimization_Project.ipynb (primary code-space)
  • Resources, containing (hospital CSV and other static data)
  • Scripts, containing python files
• GitHub repository used for storing snapshots, while live coding collaboration happens in Colab.

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Current Status

• Phase 1 complete: hospitals integrated.
• Starting Phase 2: route optimization using Dijkstra’s algorithm.