Destination choice modeling with spatially distributed constraints

Basil Vitins; Alexander Erath

doi:10.5198/jtlu.2023.2226

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2023-07-03

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Vol. 16 No. 1 (2023)

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Articles

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How to Cite

Vitins, B., & Erath, A. (2023). Destination choice modeling with spatially distributed constraints. Journal of Transport and Land Use, 16(1), 241–265. https://doi.org/10.5198/jtlu.2023.2226

Destination choice modeling with spatially distributed constraints

Basil Vitins

ASE (Analysis Simulation Engineering) AG

https://orcid.org/0000-0001-6200-5155

Alexander Erath

FHNW University of Applied Sciences and Arts

DOI: https://doi.org/10.5198/jtlu.2023.2226

Keywords: Destination, choice, model, transport, shadow, prices

Abstract

Destination choice models are a key component of any transport and land-use model. Applications in agent-based models allow for destination choice on an individual level including personal variables, like trip purpose, or situational variables. Commonly applied methodologies stem from econometrics, discrete choice theory and utility maximization using either revealed or stated preference data. This paper presents a framework to integrate cross-section ﬂows between distinct geographic areas, which can be obtained from cordon surveys or mobile phone data. Proposed optimization methodology—based on extended shadow price theory—accommodates these complementary data sources as spatially distributed constraints, in addition to the destination capacity constraints such as workplaces.

The new generic and robust optimization methodology accounts for constraints as observed on cross-section ﬂows and destination capacities while maintaining econometric choice model theory. As a proof of concept, the suggested methodology is successfully applied in a real-case, agent-based application covering the tri-national Basel region with about 2 million residents, and a large set of 2 · 10⁴ distinct destination alternatives. Due to diﬀerent wage levels in all three countries and other reasons, the region’s cross-border commuter ﬂows are highly asymmetric. Including data on cross-border ﬂows obtained from a cordon survey, the choice model’s mean deviation declines by 20% and more on a cross-section level and even more so on a choice alternative level, compared to calculations ignoring shadow prices. Moreover, multiple scenario calculations show considerable improvements in planning and forecasting applications. The results demonstrate the suitability and relevance of the proposed approach to optimize destination choice models with limited destination capacities in geographical regions usually characterized by travel demand asymmetries.

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