During public transport disruptions, travellers must replan their journey. Creating a need for personalised decision support to reduce cognitive load. We frame this as a HCI problem focused on travellers’ needs, constraints, and decision processes. We examine how contextual factors shape preferences for alternative routes using a stated-preference discrete-choice experiment that manipulates weather, disruption certainty, travel direction, nearby amenities, and route attributes. Multinomial and mixed logit models reveal substantial preference heterogeneity. Travel time and crowdedness are the strongest deterrents on average, while preferences for waiting, transferring, or switching modes vary widely. Context systematically shifts preferences: adverse weather increases acceptance of transfers and mode switches, certainty increases willingness to wait, commuting increases tolerance for crowding, and nearby amenities make waiting more acceptable. However, predictive performance was modest. These findings show that one-size-fits-all recommendations underperform; effective decision support should adapt to context, communicate uncertainty, and leverage environmental affordances to reduce cognitive burden.