Mapping the Future: Willingness-based enhanced population synthesis model framework

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This study examines the limitations of traditional population synthesis models, which often neglect workplace location in population forecasting and generate independent projections across forecast years. To enhance forecasting accuracy, an extended population synthesis model is introduced, integrating the job-housing origin-destination (OD) matrix and individuals’ willingness to change jobs and residences. The model incorporates these factors to produce a more dynamic and realistic representation of population distribution and mobility trends. Developed using the existing job-housing OD matrix and transition willingness data, the model initially synthesizes population data through the Iterative Proportional Updating (IPU) algorithm. It then applies the OD matrix as a constraint, employing probability sampling without replacement to assign workplaces to workers, ensuring consistency with actual traffic analysis zone (TAZ) statistics. An attribute database of residential and workplace locations is established. Using survey data on job and residential mobility preferences, characteristic parameters are calibrated, and a database of transition tendencies is created. Leveraging outputs from the enhanced population synthesis model and transition propensity database, a population transition model is constructed, generating annual population projections based on stock and flow perspectives. Empirical analysis demonstrates that the model effectively tracks changes in job and residential locations, maintaining spatial-temporal continuity and providing a robust foundation for studying commuting and travel behavior.