European cities are implementing diverse strategies to curtail car usage. Understanding the impact of these policies necessitates insights into mode choice behaviour. However, for conventional discrete choice models, utility specifications must be defined upfront, potentially leading to misleading policy recommendations. This problem is solved by Supervised machine learning (ML) models. However, they are challenging to interpret, which is crucial for evaluating transportation policies. We employ Shapley additive explanations (SHAP), a model- agnostic explainable artificial intelligence (XAI) tool to address this gap. The main advantages of SHAP are its foundation in game theory, the ability to highlight individual taste heterogeneity and non-linear effects. This paper aims to shed light on the potential of SHAP to improve current transportation mode choice models. Using a random forest (RF) model with the TreeSHAP estimation method, we compare SHAP insights with those derived from a traditional multinomial logit (MNL) model. The results indicate that SHAP can detect the absolute importance of features. Substantial preference heterogeneity for car choice is perceived for features reducing car usage, as opposed to features increasing car usage. Non-linear effects, such as reciprocal functions and clustered patterns, are observed for certain features. MNL and RF models disagree on the importance and heterogeneity of features, and the MNL model fails to model highly nonlinear effects. For policymakers, insights suggest that increasing parking fees and promoting car sharing may be feasible options. However, the efficiency of these measures may vary due to preference hetero- geneity. The results underscore the need for further investigation into the reasons behind the different model results and different behaviour notions of SHAP, MNL and RF.