This paper describes the conceptual development, operatonalization and empirical testing of : A Learning-based Transportation Oriented Simulation System. This activity-based model of activity-travel behavior is derived from theories of choice heuristics that consumers apply when making decisions in complex environments. The model, one of the most comprehensive of its kind, predicts which activities are conducted when, where, for how long, with whom, and the transport mode involved. In addition, various situational, temporal, spatial, spatial-temporal and institutional constraints are incorporated in the model. The decision tree is proposed as a formalism to represent an exhaustive set of mutually exclusive rules for each decision step in the model. A CHAID decision tree induction method is used to derive decision trees from activity diary data. The case study conducted to develop and test the model indicates that performance of the model is very satisfactory. We conclude therefore that the methodology proposed in this article is useful to develop computational process models of activity-travel choice behavior.