Public transport disruptions require travellers to make rapid replanning decisions under uncertainty, increasing cognitive burden and negatively affecting the travel experience. Supporting such decisions through personalised, context-aware recommendations requires modelling heterogeneous and context-dependent behaviour. This paper investigates how such behaviour can be modelled for route recommendations during disruptions. Using a discrete choice experiment and latent class modelling, we identify recurring decision strategies - context-adaptive, efficiency-driven rerouting, and waiting-oriented - rather than stable traveller types. Overlap between classes suggests that individuals shift strategies across situations. By framing latent classes as decision strategies, this work provides modelling insights beyond the public transport domain and positions latent class modelling as a structured step toward personalisation in context-dependent decision settings.

Recommendation algorithms support decision-making, yet their reasoning is often opaque. Explanations can improve user understanding, but the role of tone has received little attention, particularly in relation to situational context. This is especially relevant for route recommendations, where factors such as urgency shape travellers’ needs. Using a mixed-method approach (focus groups, n = 15; validation study, n = 32; online experiment, n = 150), we examine how tone is perceived across contextual conditions in public transport. Unlike prior work comparing tone across domains and users, we focus on within-domain, situational context-dependent effects. Results show that situational context affects tone preferences. In urgent situations, humorous and empathetic tones are less appreciated than neutral or authoritative ones, and some tones are more context-sensitive than others. Our findings emphasise considering situational context when designing recommendation explanations, and offer guidance for designers and researchers.

Previous work on Social Comparison Theory shows that comparing oneself to others can lead to negative self-perceptions and rumination, reducing self-confidence. Despite these harmful effects, social comparisons are frequently used as engagement strategies in personal informatics systems, such as health and wellness apps. There is limited understanding of how users perceive these comparisons and their impact on wellbeing. To address this, we reviewed the Top 50 Health & Wellness smartphone applications to analyse implemented comparison strategies and the metrics such comparisons are used for. We conducted an online vignette study (n=192) and an interview study (n=12) to further explore the impact of social comparisons on users. Our study shows that comparisons in personal informatics motivate users but simultaneously lead to negative emotions (e.g., inferiority, disappointment), potentially leading to obsessive thoughts and overtraining. Based on our findings, we propose design guidelines for implementing social comparison features that prioritise users' wellbeing.

Decision-making aided by Artificial Intelligence in high-stakes domains such as law enforcement must be informed and accountable. Thus, designing explainable artificial intelligence (XAI) for such settings is a key social concern. Yet, explanations are often misunderstood by end-users due to being overly technical or abstract. To address this, our study engaged with police employees in the Netherlands, who are users of a text classifier. We found that for them, usability and usefulness are of great importance in explanation design, whereas interpretability and understandability are less valued. Further, our work reports on how design elements included in machine learning model explanations are interpreted. Drawing from these insights, we contribute recommendations that guide XAI system designers to cater to the specific needs of specialized users in high-stakes domains and suggest design considerations for machine learning model explanations aimed at domain experts.

The way we experience time is based on its psychological value. Time spent efficiently or pleasantly positively influences the time experience. When choosing a transportation mode, people highly value flexibility, reliability, and autonomy. Public transport, although used frequently, does not always fulfill these values. With personalization, the public transport experience could become more efficient and pleasant. This Ph.D. research aims to personalize public transport information systems (PTIS) through context-aware communication. To achieve this goal, the following activities are planned: (1) a systematic literature review of personalization in public transport; (2) creating an elaborate passenger context model based on existing literature, expert reviews, and user studies; (3) applying this model to offer context-aware information, advice, and inspiration to public transport passengers. At the time of writing, an initial version of the context model has been produced. The next step will be validating and improving the model.