Resumen
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 ?ows 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.