Measuring impacts of condition variables in rule-based models of space-time choice behavior: Method and empirical illustration

The use of rule-based systems for modeling space-time choice has gained increasing research interests over the last years. The potential advantage of the rule-based approach is that it can handle interactions between a large set of predictors. Decision tree induction methods are available and have been explored for deriving rules from data. However the complexity of the structures that are generated by such knowledge discovery methods hampers an interpretation of the rule-set in behavioral terms with as a consequence that the models typically remain a black box. To solve this problem, this paper develops a method for measuring the size and direction of the impact of condition variables on the choice variable as predicted by the model. The paper illustrates the method based on location and transport-mode choice models that are part of Albatross model-an activity-based model of space-time choice.