This repository contains the code to create an open data synthetic population for Bavaria, Germany. It has been a joint project result between IRT SystemX and TU Munich.
It is based on a methodolgy that was originally developed for the Île-de-France region in Paris:
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.
This repository contains the code to create an open data synthetic population of Bavaria. It takes as input several publicly available data sources to create a data set that closely represents the socio-demographic attributes of persons 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 Bavaria 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!
This pipeline fulfils two purposes: First, to create synthetic populations of Bavaria in CSV and GLPK format including 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: