Integrated multiresolution framework for spatialized population synthesis

Large-scale agent-based microsimulation platforms, increasingly used in transportation demand modelling, require fully enumerated and spatialized lists of the population and its sociodemographic characteristics as input. The quality of the synthetic population, measured as its ability to reproduce the sociodemographic characteristics of the real population and their spatial distributions, is thus a determinant factor of the model reliability. While many efforts were devoted to improving the sociodemographic accuracy of synthetic populations, less attention was paid to perfecting their spatial precision. Conventional spatialized population synthesis methods, where the generation and spatialization processes are separated, are vulnerable to inconsistencies between zonal synthetic populations, and the built environments on which they are then distributed. These methods also present transferability issues that lie in their high reliance on rich spatialized datasets and knowledge of the local context. Hence, we propose an integrated multiresolution framework (IMF) that overcomes the limitations of the conventional framework (CF) by its ability to directly generate synthetic populations at the building resolution with minimal data requirements. The IMF includes an extension of an optimization-based method to multiresolution applications where any number and aggregation of spatial resolutions can efficiently be controlled. The CF and the IMF are applied to generate synthetic populations for Montreal, Canada. We define and measure sociodemographic accuracy, spatial precision, overall quality, and building-resolution fit of the synthetic populations to compare the frameworks’ performances. Despite a small loss in accuracy, the IMF achieves drastically better spatial precision, overall quality and building-resolution fit of synthetic populations, compared to the CF.