Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory

Abstract

Modeling individuals’ travel decision making in terms of choosing transport modes, route and departure time for daily activities is an indispensable component for transport system optimization and management. Conventional approaches of modeling travel decision making suffer from presumed model structures and parametric specifications. Emerging machine learning algorithms offer data-driven and non-parametric solutions for modeling travel decision making but encounter extrapolation issues (i.e., disability to predict scenarios beyond training samples) due to neglecting behavioral mechanisms in the framework. This study proposes an extrapolation-enhanced approach for modeling travel decision making, leveraging the complementary merits of ensemble machine learning algorithms (Random Forest in our study) and knowledge-based decision-making theory to enhance both predictive accuracy and model extrapolation. The proposed approach is examined using three datasets about travel decision making, including one estimation dataset (for cross-validation) and two test datasets (for model extrapolation tests). Especially, we use two test datasets containing extrapolated choice scenarios with features that exceed the ranges of training samples, to examine the predictive ability of proposed models in extrapolated choice scenarios, which have hardly been investigated by relevant literature. The results show that both proposed models and the direct application of Random Forest (RF) can give quite good predictive accuracy (around 80%) in the estimation dataset. However, RF has a deficient predictive ability in two test datasets with extrapolated choice scenarios. In contrast, the proposed models provide substantially superior predictive performances in the two test datasets, indicating much stronger extrapolation capacity. The model based on the proposed framework could improve the precision score by 274.93% than the direct application of RF in the first test dataset and by 21.9% in the second test dataset. The results indicate the merits of the proposed approach in terms of prediction power and extrapolation ability as compared to existing methods.