ile-de-france
An open synthetic population of Île-de-France for agent-based transport simulation
Science Score: 64.0%
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Repository
An open synthetic population of Île-de-France for agent-based transport simulation
Basic Info
Statistics
- Stars: 61
- Watchers: 4
- Forks: 80
- Open Issues: 19
- Releases: 1
Topics
Metadata Files
README.md
An open synthetic population of Île-de-France

This repository contains the code to create an open data synthetic population of the Île-de-France region around in Paris and other regions in France.
Main reference
The main research reference for the synthetic population of Île-de-France is:
Hörl, S. and M. Balac (2021) Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data, Transportation Research Part C, 130, 103291.
What is this?
This repository contains the code to create an open data synthetic population of the Île-de-France region around in Paris and other regions in France. It takes as input several publicly available data sources to create a data set that closely represents the socio-demographic attributes of persons and households in the region, as well as their daily mobility patterns. Those mobility patterns consist of activities which are performed at certain locations (like work, education, shopping, ...) and which are connected by trips with a certain mode of transport. It is known when and where these activities happen.
Such a synthetic population is useful for many research and planning applications. Most notably, such a synthetic population serves as input to agent-based transport simulations, which simulate the daily mobility behaviour of people on a spatially and temporally detailed scale. Moreover, such data has been used to study the spreading of diseases, or the placement of services and facilities.
The synthetic population for Île-de-France can be generated from scratch by everybody who has basic knowledge in using Python. Detailed instructions on how to generate a synthetic population with this repository are available below.
Although the synthetic population is independent of the downstream application or simulation tool, we provide the means to create an input population for the agent- and activity-based transport simulation framework MATSim.
This pipeline has been adapted to many other regions and cities around the world and is under constant development. It is released under the GPL license, so feel free to make adaptations, contributions or forks as long as you keep your code open as well!
Documentation
Summary: This pipeline fulfils two purposes: First, to create synthetic populations of French regions in CSV and GLPK format including households, persons and their daily localized activities. Second, the pipeline makes use of infrastructure data to generate the inputs to agent-based transport simulations. These steps are described in the following documents:
- How to create a synthetic population of Île-de-France
- How to run a MATSim simulation of Île-de-France
Furthermore, we provide documentation on how to make use of the code to create popuations and run simulations of other places in France. While these are examples, the code can be adapted to any other scenarios as well:
Publications
- Hörl, S. and M. Balac (2021) Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data, Transportation Research Part C, 130, 103291.
- Hörl, S. and M. Balac (2021) Open synthetic travel demand for Paris and Île-de-France: Inputs and output data, Data in Brief, 107622.
- Hörl, S., M. Balac and K.W. Axhausen (2019) Dynamic demand estimation for an AMoD system in Paris, paper presented at the 30th IEEE Intelligent Vehicles Symposium, Paris, June 2019.
- Hörl, S. (2019) An agent-based model of Île-de-France: Overview and first results, presentation at Institut Paris Region, September 2019.
Versioning
The current version of the pipeline can be found in https://github.com/eqasim-org/ile-de-france/releases. You can obtain it by cloning
the respective tag of this repository. Alternatively, you can also clone the
develop branch to make use of the latest developments.
Note that whenever you create a population with this pipeline, the meta.json
in the output will let you know the exact git commit with which the
population was created.
Owner
- Name: eqasim-org
- Login: eqasim-org
- Kind: organization
- Repositories: 8
- Profile: https://github.com/eqasim-org
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
title: "eqasim"
version: 1.5.0
url: "https://github.com/eqasim-org/ile-de-france/"
preferred-citation:
type: article
authors:
- family-names: "Hörl"
given-names: "Sebastian"
orcid: "https://orcid.org/0000-0002-9018-432X"
- family-names: "Balac"
given-names: "Milos"
orcid: "https://orcid.org/0000-0002-6099-7442"
doi: "10.1016/j.trc.2021.103291"
journal: "Transportation Research Part C: Emerging Technologies"
month: 9
start: 103291
title: "Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data"
volume: 130
year: 2021
GitHub Events
Total
- Create event: 32
- Release event: 1
- Issues event: 61
- Watch event: 11
- Delete event: 27
- Member event: 2
- Issue comment event: 146
- Push event: 123
- Pull request review event: 20
- Pull request review comment event: 17
- Pull request event: 96
- Fork event: 15
Last Year
- Create event: 32
- Release event: 1
- Issues event: 61
- Watch event: 11
- Delete event: 27
- Member event: 2
- Issue comment event: 146
- Push event: 123
- Pull request review event: 20
- Pull request review comment event: 17
- Pull request event: 96
- Fork event: 15
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Sebastian Hörl | h****n@g****m | 86 |
| Sebastian Hörl | s****l@i****r | 20 |
| Valentin LE BESCOND | N****v | 9 |
| ainar | a****r@h****r | 4 |
| Milos Balac | m****c@i****h | 4 |
| Leonardo Luquezi | 5****i | 2 |
| Arthur Burianne | 1****e | 2 |
| lubaso | 4****o | 1 |
| ouassimm | 3****m | 1 |
| Wanji Zhu | k****u@g****m | 1 |
| Clarissa | 4****v | 1 |
| firefly-cpp | i****k@i****u | 1 |
| tkchouaki | t****i@g****m | 1 |
| dependabot[bot] | 4****] | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 97
- Total pull requests: 147
- Average time to close issues: 7 months
- Average time to close pull requests: 11 days
- Total issue authors: 22
- Total pull request authors: 18
- Average comments per issue: 1.92
- Average comments per pull request: 0.97
- Merged pull requests: 110
- Bot issues: 0
- Bot pull requests: 2
Past Year
- Issues: 28
- Pull requests: 60
- Average time to close issues: about 2 months
- Average time to close pull requests: 14 days
- Issue authors: 7
- Pull request authors: 7
- Average comments per issue: 0.96
- Average comments per pull request: 0.9
- Merged pull requests: 36
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- sebhoerl (50)
- Nitnelav (11)
- vincent-leblond (7)
- neda-git (4)
- aburianne (3)
- kensongzhu (3)
- tkchouaki (2)
- Jrmwelli (2)
- ainar (2)
- syhwawa (1)
- Nasheor (1)
- vvendi (1)
- LotteNotelaers (1)
- diallitoz (1)
- BaptisteLeroux (1)
Pull Request Authors
- sebhoerl (77)
- Nitnelav (19)
- MarieMcLaurent (11)
- leo-desbureaux-tellae (8)
- LucasJavaudin (6)
- ainar (4)
- tkchouaki (4)
- aburianne (4)
- balacmi (3)
- LeonardoLuquezi (2)
- mlhollestelle (2)
- vincent-leblond (1)
- dependabot[bot] (1)
- kensongzhu (1)
- TjarkGall (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- actions/checkout v2 composite
- fiona 1.9.2.*
- geopandas 0.12.2.*
- matplotlib 3.7.1.*
- numba 0.56.4.*
- numpy 1.23.5.*
- openpyxl 3.1.0.*
- palettable 3.3.0.*
- pandas 1.5.3.*
- pip 23.0.1.*
- py7zr 0.20.4.*
- pytables 3.7.0.*
- pytest 7.2.2.*
- python 3.10.10.*
- scikit-learn 1.2.2.*
- scipy 1.10.1.*
- shapely 2.0.1.*
- tqdm 4.65.0.*
- xlrd 2.0.1.*
- xlwt 1.3.0.*