Over the past decade, policy makers and researchers in various disciplines have increasingly focused on school trip behavior. Active transportation modes have been promoted to students to improve their health and help mitigate traffic congestion and air pollution in urban areas. Policies that advocate greater use of active transport modes should account for child safety, which has been shown to be parents' primary concern. This paper focuses on the reciprocal impact between mode choice and escorting decisions for school trips. Decisions on escorting and modes of transport were modeled jointly through use of a data-mining method, a random forest method, and an econometric (nested logit) approach. The nested logit model has the advantage of allowing interpretation of independent variables; the random forest method provides a close fit to the data. Results show that the prediction potential of the random forest model is around twice as high as that of the nested logit model. To predict the probability of choosing the walking mode, for instance, a policy maker might underestimate results by about 40% if using the nested logit model. Results also demonstrate that independent consideration of these two decisions can result in a misleading assessment of policies.