The Rational Agent Access Model (RAAM) is a greedy optimization framework for calculating the accessibility of public resources. The model is based on the intuition that agents minimize a cost function that is composed of a congestion and travel time pieces:
- minℓ [(dℓ/sℓ)/ρ + trℓ/τ]
The congestion is simply the demand for the resource over the supply, converted to a cost by a (fixed) factor ρ. In our case -- primary healthcare in the United States, ρ is the national patient to physician ratio. The travel cost from a residence to a resource location, trℓ, is normalized by a configurable parameter, τ.
RAAM treats each demand location as a single agent, and on subsequent iterations shifts demand from the most expensive (used) to the least expensive available location. The algorithm terminates with a single cost at each location (no cheaper cost anywhere). RAAM can also be configured to allow agents to shift their demand to another agent, through "tunnels."
RAAM is implemented as a series of c++ classes, the highest level of which is exposed with cython, to python.
If c++ compiles are installed, you can build the so via
python setup.py build_ext --inplace
An example script and data for Chicago (2010 Census Tracts), chicago.py, suggests the basic functionality and file formats.
This is part of a project on accesssibility by James Saxon and Dan Snow, at the Center for Spatial Data Science and the Harris School, of the University of Chicago. Contact jsaxon@uc for more information, or if you're interested in using the tool.
A Docker container for creating the origin-destination matrices used in RAAM (trℓ) can be found at routing-container.