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Recent years have witnessed an increased focus on interpretability and theuse of machine learning to inform policy analysis and decision making. Thispaper applies machine learning to examine travel behavior and, in particular,on modeling changes in travel modes when individuals are presented with a novel(on-demand) mobility option. It addresses the following question: Can machinelearning be applied to model individual taste heterogeneity (preferenceheterogeneity for travel modes and response heterogeneity to travel attributes)in travel mode choice? This paper first develops a high-accuracy classifier topredict mode-switching behavior under a hypothetical Mobility-on-Demand Transitsystem (i.e., stated-preference data), which represents the case studyunderlying this research. We show that this classifier naturally capturesindividual heterogeneity available in the data. Moreover, the paper derivesinsights on heterogeneous switching behaviors through the generation ofmarginal effects and elasticities by current travel mode, partial dependenceplots, and individual conditional expectation plots. The paper also proposestwo new model-agnostic interpretation tools for machine learning, i.e.,conditional partial dependence plots and conditional individual partialdependence plots, specifically designed to examine response heterogeneity. Theresults on the case study show that the machine-learning classifier, togetherwith model-agnostic interpretation tools, provides valuable insights on travelmode switching behavior for different individuals and population segments. Forexample, the existing drivers are more sensitive to additional pickups thanpeople using other travel modes, and current transit users are generallywilling to share rides but reluctant to take any additional transfers.

Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach