Getting Started

This page describes how to get started with ActivitySim.

Note

ActivitySim is under active development

Installation

1. It is recommended that you install and use a conda package manager for your system. One easy way to do so is by using Anaconda 64bit Python 3, although you should consult the terms of service for this product and ensure you qualify since businesses and governments with over 200 employees do not qualify for free usage. If you prefer a completely free open source conda tool, you can download and install the appropriate version of Miniforge. If desired, select ‘add miniforge3 to the system PATH environment variable’ so typing ‘conda’ on the command line will work.

2. If you access the internet from behind a firewall, then you may need to configure your proxy server. To do so, create a .condarc file in your home installation folder, such as:

proxy_servers:
  http: http://myproxy.org:8080
  https: https://myproxy.org:8080
ssl_verify: false

3. Create a conda environment (basically a Python install just for this project) using conda Prompt (on Windows) or the terminal (macOS or Linux):

conda create -n asim python=3.9 activitysim -c conda-forge --override-channels

Note

If this command fails in July 2021, it may be because ActivitySim has not yet been fully integrated into the conda-forge repository. See “Alternative Installation Methods” below for a workaround using pip.

This command will create the environment and install all the dependencies required for running ActivitySim. It is only necessary to create the environment once per machine, you do not need to (re)create the environment for each session. If you would also like to install other tools or optional dependencies, it is possible to do so by adding additional libraries to this command. For example:

conda create -n asim python=3.9 activitysim jupyterlab larch -c conda-forge --override-channels

This example installs a specific version of Python, version 3.9. A similar approach can be used to install specific versions of other libraries as well, including ActivitySim, itself. For example:

conda create -n asim python=3.9 activitysim=1.0.2 -c conda-forge --override-channels

Additional libraries can also be installed later. You may want to consider these tools for certain development tasks:

# packages for testing
conda install pytest pytest-cov coveralls pycodestyle pytest-regressions -c conda-forge --override-channels -n asim

# packages for building documentation
conda install sphinx numpydoc sphinx_rtd_theme==0.5.2 -c conda-forge --override-channels -n asim

# packages for estimation integration
conda install larch -c conda-forge --override-channels -n asim

# packages for example notebooks
conda install jupyterlab matplotlib geopandas descartes -c conda-forge --override-channels -n asim

To create an environment containing all these optional dependencies at once, you can run the shortcut command

conda env create activitysim/ASIM -n asim

  1. To use the asim environment, you need to activate it

conda activate asim

The activation of the correct environment needs to be done every time you start a new session (e.g. opening a new conda Prompt window).

Alternative Installation Methods

If you prefer to install ActivitySim without conda, it is possible to do so with pip, although you may find it more difficult to get all of the required dependencies installed correctly. If you can use conda for the dependencies, you can get most of the libraries you need from there:

# required packages for running ActivitySim
conda install cytoolz numpy pandas psutil pyarrow numba pytables pyyaml openmatrix requests -c conda-forge

# required for ActivitySim version 1.0.1 and earlier
pip install zbox

And then simply install just activitysim with pip.

pip install activitysim

If you are using a firewall you may need to add --trusted-host pypi.python.org --proxy=myproxy.org:8080 to this command.

For development work, can also install ActivitySim directly from source. Clone the ActivitySim repository, and then from within that directory run:

pip install . -e

The “-e” will install in editable mode, so any changes you make to the ActivitySim code will also be reflected in your installation.

Installing from source is easier if you have all the necessary dependencies already installed in a development conda environment. Developers can create an environment that has all the optional dependencies preinstalled by running:

conda env create activitysim/ASIM-DEV

If you prefer to use a different environment name than ASIM-DEV, just append –name OTHERNAME to the command. Then all that’s left to do is install ActivitySim itself in editable mode as described above.

Note

ActivitySim is a 64bit Python 3 library that uses a number of packages from the scientific Python ecosystem, most notably pandas and numpy.

As mentioned above, the recommended way to get your own scientific Python installation is to install 64 bit Anaconda, which contains many of the libraries upon which ActivitySim depends + some handy Python installation management tools.

Anaconda includes the conda command line tool, which does a number of useful things, including creating environments (i.e. stand-alone Python installations/instances/sandboxes) that are the recommended way to work with multiple versions of Python on one machine. Using conda environments keeps multiple Python setups from conflicting with one another.

You need to activate the activitysim environment each time you start a new command session. You can remove an environment with conda remove -n asim --all and check the current active environment with conda info -e.

For more information on Anaconda, see Anaconda’s getting started guide.

Run the Primary Example

ActivitySim includes a Command Line Interface for creating examples and running the model.

To setup and run the primary example (see Examples), do the following:

  • Open a command prompt

  • Activate the conda environment with ActivitySim installed (i.e. conda activate asim)

  • Type activitysim create -e example_mtc -d test_example_mtc to copy the very small MTC example to a new test_example_mtc directory

  • Change to the test_example_mtc directory

  • Type activitysim run -c configs -o output -d data to run the example

  • Review the outputs in the output directory

Note

Common configuration settings can be overridden at runtime. See activitysim -h, activitysim create -h and activitysim run -h. ActivitySim model runs can be configured with settings file inheritance to avoid duplicating settings across model configurations. See Command Line Interface for more information.

Additional examples, including the full scale MTC regional demand model, estimation integration examples, multiple zone system examples, and examples for agency partners are available for creation by typing activitysim create -l. To create these examples, ActivitySim downloads the (large) input files from the ActivitySim resources repository. See Examples for more information.

Try the Notebooks

ActivitySim includes a Jupyter Notebook recipe book with interactive examples. To run a Jupyter notebook, do the following:

Hardware

The computing hardware required to run a model implemented in the ActivitySim framework generally depends on:

  • The number of households to be simulated for disaggregate model steps

  • The number of model zones (for each zone system) for aggregate model steps

  • The number and size of network skims by mode and time-of-day

  • The number of zone systems, see example_multiple_zones

  • The desired runtimes

ActivitySim framework models use a significant amount of RAM since they store data in-memory to reduce data access time in order to minimize runtime. For example, the example MTC Travel Model One model has 2.7 million households, 7.5 million people, 1475 zones, 826 network skims and has been run between one hour and one day depending on the amount of RAM and number of processors allocated. See Multiprocessing and Chunk for more information.

Note

ActivitySim has been run in the cloud, on both Windows and Linux using Microsoft Azure. Example configurations, scripts, and runtimes are in the other_resources\example_azure folder.

MKL Settings

Anaconda Python on Windows uses the Intel Math Kernel Library for many of its computationally intensive low-level C/C++ calculations. By default, MKL threads many of its routines in order to be performant out-of-the-box. However, for ActivitySim multiprocessing, which processes households in parallel since they are largely independent of one another, it can be advantageous to override threading within processes and instead let ActivitySim run each process with one computing core or thread. In order to do so, override the MKL number of threads setting via a system environment variable that is set before running the model. In practice, this means before running the model, first set the MKL number of threads variable via the command line as follows: SET MKL_NUM_THREADS=1