Destination choice modeling with spatially distributed constraints

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.