EvacSim

EvacSim is a deterministic simulation that uses graph theory and linear programming to model the effects of natural disasters on communities in order to find optimal evacuation routes. This project was originally developed for HackRPI 2019, a 24-hour hackathon in which it won 2nd place. The project has now been overhauled as an open-source platform, in the hopes that it will become a service that can be used for the greater good.

Background

This project was originally developed for HackRPI 2019, a 24-hour hackathon in which it won 2nd place. It used Java for model translation and representation, AMPL to run the flow algorithm, and Node to hold the whole thing together. This has since been replaced entirely by Python.

The idea for the project was born after witnessing the devastating effects of Hurricane Dorian as well as hearing from friends and family in tropical regions who were forced to evacuate their homes due to storms numerous times over the years. As such, the application was initially only able to simulate hurricanes. This has since been replaced by a generic natural disaster representation, modeled by polygons corresponding to the disaster's area of effect at different times.

How it works

Model

In the model, populations are represented by nodes and the infrastructure between them are represented by edges. These nodes and edges can then be used to construct a graph. The actual modeling happens in CSV files, such as the following nodes.csv file modeling populations around Troy, NY:

Enabled	Name	Latitude	Longitude	Population	Capacity
0	Troy	42.718	-73.687	50000	60000
0	Albany	42.649	-73.753	100000	125000
0	Guilderland	42.702	-73.909	35000	50000
0	East Greenbush	42.588	-73.703	15000	20000
0	Brunswick	42.732	-73.562	15000	30000
0	Schenectady	42.806	-73.943	65000	80000
0	Latham	42.748	-73.761	20000	30000

Similar models exist for edges and natural disasters:

• edges.csv: Models infrastructure (ie. roads) between populations by source node, destination node, transit time, and maximum capacity.
• disaster.csv: Models the natural disaster with polygons, representing its area of effect at different times. Area of effect polygons can have any number of sides, as long as they are ordered clockwise or counter-clockwise.

Examples can be found in the models folder.

Simulation

Once the models have been read, an algorithm is run on each city within the natural disaster's area of effect. This algorithm is similar to a minimum-cost flow algorithm: it aims to find an evacuation route each at-risk community can take to reach a safe city in the least amount of time, without violating constraints like maximum capacities on cities and infrastructure.

Results

After running the simulation, the results can be exported to a KML file. This file can be viewed in an Earth browser such as Google Earth, where the cities, infrastructure, natural disaster, and evacuation routes can be explored, as well as their supporting data.

Setting up

1. Install Python 3 through the installer or through a package manager like Brew or APT depending on your OS
2. Install the virtualenv package with pip3 install virtualenv
3. Create a new virtual environment with python3 -m virtualenv .venv
4. Activate the virtual environment with source .venv/bin/activate on macOS/Linux or .venv\Scripts\activate on Windows
5. Install the project's dependencies with pip3 install -r requirements.txt
6. You can now run the application with python3 evacsim/__init__.py, specifying arguments as necessary
7. When you're finished, deactivate the virtual environment with deactivate