This paper presents a step-by-step method to generate a synthetic population for agent-based transport modelling asinput to MATSim software, which requires an activity chain for each agent. We make use of high spatial resolutionstatistical raster (250 m) census data, applying all calculations at this scale. Due to the small sample, size of travel surveydata an Iterative Proportional Fitting method is not suitable. Therefore, we devise a method utilizing Bayesian networks,maximum likelihood and Markov Chain Monte Carlo simulation to reproduce attribute distribution and fit to rastermargins. Stratified sampling along households is employed to generate activity chains for the synthetic population.
|Number of pages||9|
|Journal||International Journal of Traffic and Transportation Management|
|Publication status||Published - 2020|