  Building an accurate model of travel behaviour based on individuals'
characteristics and built environment attributes is of importance for
policy-making and transportation planning. Recent experiments with big data and
Machine Learning (ML) algorithms toward a better travel behaviour analysis have
mainly overlooked socially disadvantaged groups. Accordingly, in this study, we
explore the travel behaviour responses of low-income individuals to transit
investments in the Greater Toronto and Hamilton Area, Canada, using statistical
and ML models. We first investigate how the model choice affects the prediction
of transit use by the low-income group. This step includes comparing the
predictive performance of traditional and ML algorithms and then evaluating a
transit investment policy by contrasting the predicted activities and the
spatial distribution of transit trips generated by vulnerable households after
improving accessibility. We also empirically investigate the proposed transit
investment by each algorithm and compare it with the city of Brampton's future
transportation plan. While, unsurprisingly, the ML algorithms outperform
classical models, there are still doubts about using them due to
interpretability concerns. Hence, we adopt recent local and global
model-agnostic interpretation tools to interpret how the model arrives at its
predictions. Our findings reveal the great potential of ML algorithms for
enhanced travel behaviour predictions for low-income strata without
considerably sacrificing interpretability.
