The rapid and inevitable growth of availability of travel information for travellers has increased expectations among policy makers about the benefits of travel information. It is increasingly expected that providing advanced travel information can trigger particular travel behaviors that would contribute to sustainable mobility. Currently, travel information is mostly descriptive and distributed to a group of travellers. This kind of information just provides information about different alternatives without emphasizing which alternative is better to choose. In addition, it ignores differences between individuals. However, to induce travellers to behave in particular ways it may be more effective to recommend one best alternative, which is in line with their preferences and habitual activity-travel pattern. In that sense, the increasing availability of smart phones allows one to issue context-sensitive, personal advice. As a result, dedicated personalized recommendations could be provided, considering personal preferences and optimal control strategies. Such new technology, however, requires advanced data collection and a new generation of models about traveller strategic responses. In that regard, stated adaptation experiments are a proper approach to collect data when the technology still is not available to use in practice. In this paper, we evaluate effects of personalized travel information on individuals’ activity-travel behavior. To identify those effects, we introduce an innovative stated adaptation approach to assess possible behavioral changes in the presence of advanced forms of travel information. In the proposed SA approach, first, a detailed profile of individuals’ activity-travel pattern for one day is collected. Second, different scenarios are given to subjects, who are asked how they would change their activity-travel pattern under information provision. The provided travel information to each individual is either descriptive or prescriptive. Four scenarios are assigned to each individual. Each scenario is based on attributes of four different. Results of data analysis provide insights into the differential effects of descriptive and prescriptive travel information on activity-travel patterns. In turn, any induced change will provide keys to the effectiveness of travel information for transport demand management.