## 1 Introduction  
The achievement of global climate goals forces society to increase the share of sustainable transport in total traffic [1]. In urban environments, active modes of transport like walking or cycling are especially promoted [1]. As the chosen mode of transport depends on the perceived user experience [2], the user experience of sustainable mobility solutions needs to be brought into focus. Therefore, it becomes necessary to implement user experience and associated assessment methods in the development process of sustainable modes of transport. However, the subjective assessment of user experience suffers limited validity, as the self-assessment is biased by social desirability [3], measures only conscious processes [4] and is usually measured with a time delay [5]. The objective measurement of user experience is equally considered challenging [6]. Overcoming the disadvantages of the individual use of either subjective or objective methods of measurement, user experience in transport is quantified using objective data. Traditional quantification methods are parametric regression techniques such as multiple linear regression or logistic regression [5, 7]. The regression coefficients are considered easy to interpret, allowing to determine how the predictors impact the model output. However, the use of traditional regression techniquesis subject to strict assumptions such as linearity between input and output variables, providing only a good fit if these assumptions are met [8]. Complex real-world applications are non-linear and therefore cannot be fully represented by these types of regression [9; 10]. Machine learning methods have become increasingly popular for quantification tasks, providing better model performance compared to traditional regression models [11; 12]. In transport, several studies applied machine learning methods to predict subjective experiences of cyclists [13; 14] or car drivers [15]. However, the improved prediction performance is at the expense of transparency [11; 12]. The black-box character of machine learning algorithms leaves the contribution of individual predictors on the model output indefinite. Transport researchers seek a holistic causal understanding to determine how individual predictors contribute to the overall model output [10; 16]. To understand the user experience of sustainable transport, the identification of facilitators and barriers is crucial. Explainable AI (XAI) methods can close this gap by providing post hoc methods to improve explainability. In transport, XAI is receiving increasing attention, offering promising avenues for exploring transport solutions [17; 18; 19]. Although XAI has become popular for evaluating behavioural or environmental factors in transport, it has just as much potential for improving the user experience of transport solutions. However, to the author's knowledge, no work has been carried out that evaluates subjective experiences in transport using XAI. Following the need to enhance user experience of sustainable transport solutions, we introduce a novel approach to explore user experience with XAI methods. In the present paper, we applied the method to the specific use case of e-bike riding, being a rapidly growing mode of sustainable transport [20]. In the present study, we explored the riding experience using the popular explainable AI method SHAP (SHapley Additive exPlanations) [21]. We aimed to assess the potential of XAI to explore user experience in transport. Applied to the use case of e-bikes, our goal was to explore factors influencing the riding experience on e-bikes. We chose Gaussian process regression (GPR) to quantify riding experience because of its ability to model a variety of linear and non-linear relationships between input and output variables [22]. In a cycling study, we collected both sensor data including rider behaviour, motor power and riding dynamics and self-assessed riding experience.