R5 is Conveyal's routing engine for multimodal (transit/bike/walk/car) networks, with a particular focus on public transit. It is intended primarily for analysis applications (one-to-many trees, travel time matrices, and cumulative opportunities accessibility indicators).
We refer to the routing method as "realistic" because it works by planning many trips at different departure times in a time window, which better reflects how people use transportation system than planning a single trip at an exact departure time. R5 handles both scheduled public transit and headway-based lines, using novel methods to characterize variation and uncertainty in travel times.
We say "Real-world and Reimagined" networks because R5's networks are built from widely available open OSM and GTFS data describing baseline transportation systems, but R5 includes a system for applying light-weight patches to those networks for immediate, interactive scenario comparison.
R5 is a core component of Conveyal Analysis, which allows users to create transportation scenarios and evaluate them in terms of cumulative opportunities accessibility indicators. See the methodology section of the Conveyal user manual for more information.
Please note that the Conveyal team does not provide technical support for third-party deployments of its analysis platform. We provide paid subscriptions to a cloud-based deployment of this system, which performs these complex calculations hundreds of times faster using a compute cluster. This project is open source primarily to ensure transparency and reproducibility in public planning and decision making processes, and in hopes that it may help researchers, students, and potential collaborators to understand and build upon our methodology.
For details on the core methods implemented in Conveyal Analysis and R5, see:
The Conveyal team is always eager to see cutting-edge uses of our software, so feel free to send us a copy of any thesis, report, or paper produced using this software. We also ask that any academic publications using this software cite the papers above, where relevant and appropriate.
It is possible to run a Conveyal Analysis UI and backend locally (e.g. on your laptop), which should produce results identical to those from our hosted platform. However, the computations for more complex analyses may take quite a long time. Extension points in the source code allow the system to be tailored to cloud computing environments to enable faster parallel computation.
To get started, copy the template configuration (
To run locally, use the default values in the template configuration file.
offline=true will create a local instance that avoids cloud-based storage, database, or authentication services.
By default, analysis-backend will use the
analysis database in a local MongoDB instance, so you'll also need to install and start a MongoDB instance.
Database configuration variables include:
database-uri: URI to your MongoDB cluster
database-name: name of the database to use in your MongoDB cluster
Once you have configured
analysis.properties and started MongoDB locally, you can build and run the analysis backend with
gradle runBackend. If you have checked out a commit (such as a release tag) where you are sure all tests will pass, you can skip the tests with
gradle -x test runBackend.
You can build a single self-contained JAR file containing all the dependencies with
gradle shadowJar and start it with
java -Xmx2g -cp build/libs/r5-vX.Y.Z-all.jar com.conveyal.analysis.BackendMain.
Once you have this backend running, follow the instructions to start the analysis-ui frontend. Once that the UI is running, you should be able to log in without authentication (using the frontend URL, e.g. http://localhost:3000).
In order to do development on the frontend or backend, you'll need to set up a local development environment. We use IntelliJ IDEA. The free/community edition is sufficient for working on R5. Import R5 into IntelliJ as a new project from existing sources. You can then create a run configuration for
com.conveyal.analysis.BackendMain, which is the main class. You will need to configure the JVM options and properties file mentioned above.
By default, IntelliJ will follow common Gradle practice and build R5 using the "Gradle wrapper" approach, in which operating-system specific scripts are run that download and install a specific version of Gradle in the projet directory. We have encountered problems with this approach where IntelliJ seems to have insufficient control over the build/run/debug cycle. IntelliJ has its own internal implementation of the Gradle build process, and in our experience this works quite smoothly and is better integrated with the debug cycle. To switch to this appraoch, in the Gradle section of the IntelliJ settings, choose "Build and run using IntelliJ IDEA" and "Run tests using IntelliJ IDEA". Below that you may also want to choose "Use Gradle from specified location" to use your local system-wide copy.
We use structured commit messages to help generate changelogs and determine version numbers.
The first line of these messages is in the following format:
(<scope>) is optional and is often a class name. The
<summary> should be in the present tense. The type should be one of the following:
The body of the commit message (if any) should begin after one blank line. If the commit meets the definition of a major version change according to semantic versioning (e.g. a change in API visible to an external module), the commit message body should begin with
BREAKING CHANGE: <description>.
Presence of a
fix commit in a release should increment the number in the third (PATCH) position.
Presence of a
feat commit in a release should increment the number in the second (MINOR) position.
Presence of a
BREAKING CHANGE commit in a release should increment the number in the first (MAJOR) position.
This is based on https://www.conventionalcommits.org.