Journal of Transport and Land Use

Synthesizing activity locations in the context of integrated activity-based models

Natalia Zuniga-Garcia

Argonne National Laboratory

https://orcid.org/0000-0002-1538-3599

Pedro Veiga de Camargo

Argonne National Laboratory

https://orcid.org/0000-0001-9613-2777

DOI: https://doi.org/10.5198/jtlu.2025.2291

Keywords: Location synthesis, land use, agent-based simulation, zero-inflated negative binomial, Bayesian regression

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Abstract

Activity-based models are a powerful tool for transportation analysis and represent the future of the industry in terms of modeling techniques. However, the data-hungry aspect of these models makes them difficult and slow to build. This paper presents a set of methodologies to synthesize activity locations for U.S. cities, providing estimates of locations by land-use type in areas with limited available data. The methodology includes a regression method to estimate the number of locations by land-use type complemented by selective use of open data. Detailed information from the entire Southern California Association of Governments (SCAG) area, comprising more than 100,000 km², is used to calibrate the model. A zero-inflated negative binomial (ZINB) regression is proposed to tackle the excess of zeros in the dataset. The model is estimated using a Bayesian approach that quantifies the coefficients’ variability, uses information regarding prior beliefs, and estimates zero-inflated probabilities by zone. The main results suggest that the proposed methodological framework can be used to estimate locations in a fast and efficient way without the need for detailed land-use information. Transportation planners and policymakers can use the results and methods provided in this research to approximate activity location distributions in activity-based models.

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