KRA33-PRISMX UAM Vertiport Simulation

A discrete-event simulation platform for Urban Air Mobility (UAM) vertiport networks — models eVTOL dispatch, charging, and KPI collection, with a Streamlit dashboard, an MCP server for AI-assisted analysis, and a genetic-algorithm optimizer for vertiport network layout.

Components

File	Role
uam_engine.py	Core simulation engine — DataLoader, eVTOL, Vertiport, UAMSim, dispatch policies, KPI collection. Pure logic, no GUI.
uam_web_app.py	Streamlit dashboard for running simulations and visualizing results (maps, charts).
uam_mcp_analyst.py	MCP server exposing the simulator as tools for Claude Desktop ("Strategic Operations Analyst" — run simulations, analyze KPIs, what-if studies).
uam_worker.py	Detached background process for long-running simulation/sensitivity jobs, spawned by the MCP server.
uam_lausa.py	LAUSA — Latent-factor Analysis of Urban Suitability & Attractiveness, a pre-simulation site-selection module for scoring candidate vertiport locations.
uam_app.py	Single packaged entry point (dispatches to dashboard / --mcp-server / --worker modes) used when building the standalone executable.
C_HGA/	C++ hybrid genetic algorithm for vertiport network optimization.

Requirements

• Python 3.12
• gurobipy (requires a Gurobi license for problem sizes beyond the trial limit)
• A C++ compiler (for building C_HGA/uam_hybrid_ga.cpp, Windows/MinGW Makefile included)

pip install -r requirements.txt

Running

Dashboard (dev):

streamlit run uam_web_app.py

MCP server (for Claude Desktop):

python uam_mcp_analyst.py

Add it to Claude Desktop's MCP config (claude_desktop_config.json — accessible via Claude Desktop → Settings → Developer → Edit Config), then restart Claude Desktop.

Running from source (requires Python + dependencies installed):

{
  "mcpServers": {
    "uam-analyst": {
      "command": "python",
      "args": ["<absolute-path-to>/uam_mcp_analyst.py"]
    }
  }
}

Replace <absolute-path-to> with the full path to uam_mcp_analyst.py on your machine.

Running the packaged .exe (no Python needed — see Building the standalone executable):

{
  "mcpServers": {
    "uam-analyst": {
      "command": "<absolute-path-to>/UAMSimulator.exe",
      "args": ["--mcp-server"]
    }
  }
}

Replace <absolute-path-to> with the full path to UAMSimulator.exe (e.g. dist/UAMSimulator/UAMSimulator.exe after building, or wherever you extracted the release zip).

Packaged entry point (same three modes, used by the built .exe):

python uam_app.py                                   # dashboard
python uam_app.py --mcp-server                       # MCP server
python uam_app.py --worker <task_type> <token> <json> # background job

Building the standalone executable

pyinstaller UAMSimulator.spec

Output goes to dist/UAMSimulator/. The built app is distributed via GitHub Releases rather than committed to the repo.

Data

Sample datasets live in sample_data/ (demand data, vertiport info, LAUSA site-scoring geojson). simulation_config.json holds default simulation parameters.