Generating synthetic population with activity chains as agent-based model input using statistical raster census data

Samuel Felbermair*, Florian Lammer, Eva Trausinger-Binder, Cornelia Hebenstreit

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


Agent-based transport modelling needs more detail on the synthetic population compared to conventional transport models, as activity chains are required. In many cases, however the sample size of travel surveys from which to gain activity chains is small. Using Bayesian networks and Markov Chain Monte Carlo as well as stratified sampling, we show how a population with activities plans can be generated using limited survey data. Moreover, this paper presents a method for using statistical raster (250 m) census data for all activities and facilities, which guarantees a high spatial resolution. The synthetic population was developed for the predominantly rural to intermediately urban state of Carinthia in Austria. Realistic travel plans were assigned to each agent, considering trip dependencies between household members as well as correlations between socio-demographic attributes and travel behaviour. The resulting synthetic population includes agents with a sequence of activities for 24 hours. The activities and trip length distributions of the simulated population fit the survey data well. The simulation results fit the traffic counts.

Original languageEnglish
Pages (from-to)273-280
Number of pages8
JournalProcedia Computer Science
Publication statusPublished - 1 Jan 2020
Event11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops - Warsaw, Poland
Duration: 6 Apr 20209 Apr 2020


  • agent-based modelling
  • Bayesian Networks
  • Markov Chain Monte Carlo
  • MATSim
  • population synthesis
  • transport modelling

ASJC Scopus subject areas

  • Computer Science(all)


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