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ARTÍCULO
TITULO

Cognitive learning approach for travel demand modeling: estimation results

Sehnaz Cenani    
Theo A. Arentze    
Harry J.P. Timmermans    

Resumen

The paper reports progress in the development of an agent-based model of cognitive learning, which simulates spatial perception updating in connection with daily travel behavior based on the principle of Bayesian perception updating. This model is embedded in a multi agent-based model of activity-travel scheduling and choice behavior. The aim of this paper is to empirically estimate the proposed model using data on individuals? landmark recognition in a field survey. The main findings of the study show that the model fits the data satisfactorily and results are reasonable. The comparison between the proposed Bayesian model and a more basic binary logit model shows that the model improves when prior probabilities are taken into account, which provides evidence for the proposed Bayesian model.

PÁGINAS
pp. 55 - 64

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