Night trains benefit society in multiple ways. They are environmentally friendly, improve the accessibility of regions and are space-effective. Understanding traveller's preferences enables night train operators to improve night train services and harness societal benefits better. This study conducted a stated preference survey with 1031 respondents from the Netherlands to dive deeper into the importance of operational factors like booking convenience, travel costs, long travel times of up to 18 hours and accommodations. Additionally, factors that make up a convenient booking scenario were revealed. Lastly, a latent class choice model (LCCM) was applied to derive insights into heterogeneity and to determine to which extent personal factors influence class membership. Results reveal that for a convenient booking scenario, being able to book one ticket and comparing travel options are most important. However, booking convenience only plays a minor role in determining night train mode choice. Travel costs and accommodation are significantly more important. Several classes have been revealed: Environmentally conscious comfort lovers make up 13% of the respondents, experienced night train travellers 29\%, cost-sensitive travellers 37% and flight lovers 20%. Applying a scenario analysis, night train market shares vary from 20% to 71%, with significant heterogeneity among respondents. For practitioners, this implies focusing mainly on prices and accommodation while taking the significantly different preferences of the population into consideration.

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.