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 t...

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Veröffentlicht in:Transportation (Dordrecht) Jg. 51; H. 3; S. 823 - 852
Hauptverfasser: Khachman, Mohamed, Morency, Catherine, Ciari, Francesco
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.06.2024
Springer Nature B.V
Schlagworte:
Accuracy
Built environment
Economic Geography
Economics
Economics and Finance
Engineering Economics
Frame analysis
Innovation/Technology Management
Local knowledge
Logistics
Marketing
Optimization
Organization
Population
Populations
Regional/Spatial Science
Reliability
Sociodemographics
Spatial distribution
Synthesis
Transferability
Transportation applications
Travel demand
Urban environments
Population synthesis
Integrated framework
Multiresolution control
Travel demand modelling
Spatial disaggregation
Optimization-based method
ISSN:0049-4488, 1572-9435
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Zusammenfassung: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.
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ISSN:0049-4488
1572-9435
DOI:10.1007/s11116-022-10358-w