Cognitive learning approach for travel demand modeling: estimation results

S. Cenani, T.A. Arentze, H.J.P. Timmermans

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
118 Downloads (Pure)


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

Original languageEnglish
Pages (from-to)55-64
Number of pages10
JournalTransportation Research Procedia
Publication statusPublished - 2017


  • activity-travel behavior
  • agent-based model
  • Cognitive learning
  • dynamic urban networks
  • landmark
  • perception updating


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