Using ensembles of decision trees to predict transport mode choice decisions: effects on predictive success and uncertainty estimates

The application of activity-based models of travel demand to planning practice has triggered interest in issues that potentially improve the accuracy and/or usefulness of model forecasts. The limited knowledge of uncertainty propagation in complex stochastic model systems has put uncertainty analysis high on the research agenda to differentiate between simulation error and policy effects. Focusing on transport mode choice, this paper draws attention to the use of model ensembles, which has hardly been explored in travel demand forecasting. Prior studies predicting transport mode choice has typically relied on a single equation, relating observed transport mode choices to a set of personal and contextual variables. The estimated single model is then assumed to apply to all individuals. This paper explores the idea of replacing a single equation/representation with an ensemble of model predictions, using the decision tree formalism. Potentially, ensembles better capture the notion that travellers may use different heuristics in their transport mode decisions. The aim of the study is to investigate whether the use of a model ensemble of different decision heuristics will reduce the error/uncertainty in predicting transport mode decisions. Results of the study, conducted in the Rotterdam region, The Netherlands, suggest that the accuracy of predicting transport mode choice is improved, albeit non-monotonically, with increasing ensemble size. Simultaneously, the uncertainty related to these predictions is decreasing. Finally, it is shown that the importance of the selected explanatory variables co-varies with ensemble size. Estimation results tend to become stable in this study with an ensemble size of approximately 20 decision trees.