Resources for Computing Travel Cost

The spatial access measures depend on travel times or distances between origins and destinations. If you only need distances between origins and destinations, the package will calculate Euclidean distances for your projected data. If you need travel times for a specific travel mode, you need to generate these so-called travel time (or travel cost) matrices from other sources.

Explore and Download Pre-Computed Travel Times

Since computing travel times is computationally expensive and non-trivial at scale, we’ve pre-computed times between common Census geographies for several travel modes. These times cover the entire United States and the most recent Census years (2020+). They are available via [OpenTimes](https://opentimes.org/), a dedicated website created by Dan Snow (UChicago MPP’19). For more information on how these times are calculated, visit the [OpenTimes GitHub](https://github.com/dfsnow/opentimes).

Compute your Own Travel Times

If you need to compute customized cost matrices, there are several options. This table lists some of them:

	Name	Installation	Notes
	pgRouting	docker	Good for driving, open-source and free, PostgreSQL/postgis and OpenStreetMap (OSM)
	OSRM	install / R / docker	Best for driving, OSM, open-source and free, customized travel parameters, C++
	Open Trip Planner	docker routing / resources / DockerHub	Best for transit, open-source and free, customized travel parameters, Java
	Valhalla	install	Multi-modal, OSM, open-source, for fee at scale, Python
	Pandana	install	Good for driving and walking, OSM, open-source and free, part of UrbanSim, Python
	Graphhopper	install	Multi-modal, OSM, open-source, for fee at scale, Python
	Spatial Access Package	install / notebooks	Best for walking, OSM, scales well, open-source and free, includes spatial access metrics, Python
	e.g. dogr, R5 and gtfs-router	dogr, R5, gtfs-router	Selected R packages
	Google Maps	install	Accurate multi-modal, customized travel parameters, commercial, expensive at scale