Synthesizing Population for Microsimulation-based Integrated Transport Models Using Atlantic Canada Micro-data

Due to the lack of availability of micro-data of population characteristics, the synthesis of individual and household attributes is a necessary step for developing a disaggregate, dynamic travel demand forecasting model. Agent-based micro-simulation models attempt to forecast travel behaviour of in...

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Bibliographic Details
Published in:Procedia computer science 2014, Vol.37, p.410-415
Main Authors: Hafezi, Mohammad Hesam, Habib, Muhammad Ahsanul
Format: Article
Language:English
Subjects:
Household level
Individual level
Land Use and Transportation Micro-simulation
Population synthesis
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Summary:Due to the lack of availability of micro-data of population characteristics, the synthesis of individual and household attributes is a necessary step for developing a disaggregate, dynamic travel demand forecasting model. Agent-based micro-simulation models attempt to forecast travel behaviour of individuals and households by simulating the behaviour of the singular actors in the system. The framework for generating synthesis population presented in this paper is a fundamental contribution to the development of an Integrated Transport, Land Use and Environment Modelling System in Nova Scotia, Canada. In this paper, a population is synthesized for individuals and households in Atlantic Canada using the Fitness Based Synthesis (FBS) approach. A synthetic algorithm is designed that allows both individual and household attribute levels to synthesize simultaneously. Unequal probabilities based on the sampling weight are used in the household selection step of the algorithm. In this way, the performance and accuracy of the synthetic population produced has been improved. The synthetic algorithm is tested for two functions: first, using the one level (household) control tables; and second, using two levels (individual and household) control tables. The data used in this study is collected from the 2006 Canadian Census and the 2006 Public Use Micro-data File (PUMF). The algorithm is implemented using a high-level matrix programming language for numerical computation in MATLAB. The results show that the synthetic population with both individual and household level attributes has the best fitness value.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2014.08.061