1. An overview of AequilibraE¶
AequilibraE is the first comprehensive Python package for transportation modeling, and it aims to provide all the resources not easily available from other open-source packages in the Python (NumPy, really) ecosystem.
AequilibraE has also a fully features interface avalaible as a plugin for the open source software QGIS, which is separately mantained and discussed in detail its documentation.
AequilibraE is organized in submodules that are often derived from the traditional 4-step model. However, other modules have already been added or are expected to be added in the future. The current modules are:
Contributions can be made to the existing modules or in the form of new modules.
1.2. AequilibraE Project¶
The AequilibraE Project, as a consistent model file, comes from the vision of having a complete model system that would have capabilities to support the vast majority of analysis usually performed with traditional transport models.
In a nutshell, the AequilibraE project file is designed to behave like many of the commercial platforms available in the market, where a single file hosts the majority of the data. Differently then these platforms, however, AequilibraE is designed on top of an open data format, SQLite.
This concept is still under heavy development, but it is already possible to use its structure to download full, routable networks directly from Open Street Maps .
More detailed is provided in - The AequilibraE project.
1.3. Global parameters¶
As more features are added to AequilibraE, a large number of parameters start to be required, so the parameters module has also been growing in importance within the software.
There are currently 4 main sessions with the parameters file: Assignment, Distribution, Network and System.
The parameters for assignment and distribution control only convergence criteria, while the System section controls things like the number of CPU cores used by the software, default directories and Spatialite location for Windows systems. The Network section, however, contains parameters that control the creation of networks and the import from Open Street Maps.
1.4. Trip distribution¶
The trip distribution module is the second oldest piece of code in AequilibraE, and includes only code for calibration and application of Synthetic gravity models and Iterative Proportional Fitting. Not much documentation has been written for this module, but some examples are available on the usage examples page Trip distribution.
1.5. Path computation¶
The path computation module contains some of the oldest code in AequilibraE, some of which preceed the existance of AequilibraE as a proper Python package.
The package is built around a shortest path algorithm ported from SciPy and adapted to support proper multi-threading, network loading and multi-field skimming.
As of now, this module has three main capabilities:
Regular path computation (one origin to one destination)
It is worth noting that turn-penalties and turn-prohibitions are currently not supported in AequilibraE, and that there is no built-in support for multiple concurrent shortest paths computation, although the path computation path does release the GIL, which allows the users to get some performance gains using Python’s threading module.
A wealth of usage examples are available in the examples page under Paths module.
For now the only transit-related capability of AequilibraE is to import GTFS into SQLite/Spatialite. The results of this import is NOT integrated with the AequilibraE project.
The matrix submodule has two main components. Datasets and Matrices
Their existence is required for performance purposes and to support consistency across other modules. It also make it a lot faster to develop new features. Compatibility with de-facto open standards is also pursued as a major requirement.
They are both memory mapped structures, which allows for some nice features, but they still consume all memory necessary to handle them in full. In the future we will look into dropping that requirement, but the software work is substantial.
1.7.1. AequilibraE Matrix¶
If one looks into how all commercial software handle matrices, it would be clear that there are definitely two different schools of thought. The first one, is where matrices are simple binary blobs that require the existence of a model to give it context.
The second one is a more comprehensive take, where a matrix file not only contains its indices and metadata, but one that can also store multiple matrices. This capability, available in the openmatrix format, is one that was reproduced in the AequilibraE matrix and API.
Because the Open Matrix Format has established itself as the de-facto standard for matrix exchange in the industry, AequilibraE aims to allow users to never touch the AEM data format if they so decide. However, all underlying computation will be done using our custom format, so the importing/exporting being done under the hood will mean additional (although small) overhead.
For programatic applications where performance is critical, we recommend using the AEM format whenever possible.
1.7.2. AequilibraE Data¶
AequilibraE datasets are data structures based on NumPy record arrays (arrays with named columns). Its role in the software is to hold columnar data for all procedures that may use it, such as O/D/P/A vectors used in trip distribution and link loads from traffic assignment.
AequilibraE data currently supports export to csv and sqlite. Extending it to other binary files such as HDF5 or Arrow are being considered for future development. If you require them, please file an issue on GitHub.