Improving performance of multiagent rule-based model for activity pattern decisions with Bayesian networks

Several activity-based models are now becoming operational and are entering the stage of application in transport planning. Some of these models use a set of decision trees to support decision making instead of using principles of utility maximization. However, it is believed that the structure of decision trees can sometimes be very unstable and sensitive to highly correlated predictors. Therefore, this study examines whether decision trees constitute the best representational form to capture the behavioral mechanisms and principles that individuals and households use to organize their activities. Findings are reported from experiments conducted by means of Bayesian networks to gain a better understanding of the predictive performance of Albatross, a sequential rule-based model of activity-scheduling behavior. The performances of Bayesian networks and decision trees are compared and results are evaluated by means of detailed quantitative and qualitative analyses. The results showed that Bayesian networks outperformed the decision-tree-based approach for all decision agents of the Albatross model. Given this excellent performance, it is believed that the research community may potentially consider the use of Bayesian networks in developing activity-based transportation models