Abstract
In this paper, we develop a synthetic population as the first step in implementing an integrated land use/transport model. The model is agent-based, where every household, person, dwelling, and job is treated as an individual object. Therefore, detailed socioeconomic and demographic attributes are required to support the model. The Iterative Proportional Updating (IPU) procedure is selected for the optimization phase. The original IPU algorithm has been improved to handle three geographical resolutions simultaneously with very little computational time. For the allocation phase, we use Monte Carlo sampling. We applied our approach to the greater Munich metropolitan area. Based on the available data in the control totals and microdata, we selected 47 attributes at the municipality level, 13 attributes at the county level, and 14 additional attributes at the borough level for the city of Munich. Attributes are aggregated at the household, dwelling, and person level. The algorithm is able to synthesize 4.5 million persons in 2.1 million households in less than 1.5 h. Directions regarding how to handle multiple geographical resolutions and how to balance the amount and order of attributes to avoid overfitting are presented.
Highlights
Synthetic populations are used in transportation modeling when individual records of households and persons are not available due to privacy reasons, insufficient resolution, or missing attributes
In this paper, we develop a synthetic population as the first step in implementing an integrated land use/transport model
The Iterative Proportional Updating (IPU) procedure is selected for the optimization phase
Summary
Synthetic populations are used in transportation modeling when individual records of households and persons are not available due to privacy reasons, insufficient resolution, or missing attributes. Control attributes can be aggregated at different geographical resolutions, such as boroughs, municipalities, or counties. Synthesizing a population has two main phases: optimization (fitting) and allocation. The first phase fits a disaggregate sample of agents (microdata) to aggregated constraints (control totals), while the second phase replicates actual agents for the synthetic population using a probabilistic selection [2]. While the procedure on the second stage is usually the same across population synthesizers and relies on Monte Carlo sampling, there is a broad range of procedures for the first stage
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
