Representing Mental Maps and Cognitive Learning in Micro-Simulation Models of Activity-Travel Choice Dynamics

The mental map of an individual is formed and continuously changes over time based on observations the individual makes during the implementation of activities and trips. This paper proposes a model, based on Bayesian beliefs networks, for representing mental maps. The study shows how this model can be integrated in discrete choice models/activity-based models to simulate dynamic decision-making under uncertainty, spatial search and spatial cognitive learning. The theory underlying the model is discussed, a specification is derived and numerical simulation is used to illustrate the properties of the model. This approach has several potential advantages: (1) learning is incremental so that adaptive behavior can be modeled in a natural way; (2) beliefs are represented as probability distributions so that they can be integrated in a utility framework to model decision making in a straightforward way; and (3) beliefs are represented as probability distributions so that the degree of uncertainty and expected information gain related to choice alternatives can be quantified by means of an entropy measure. Directions for future research are discussed.

  • Availability:
  • Authors:
    • Arentze, T A
    • Timmermans, H J P
  • Publication Date: 2005-7


  • English

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  • Accession Number: 01000485
  • Record Type: Publication
  • Files: TRIS, ATRI
  • Created Date: May 30 2005 12:25PM