With economic globalization and continuous technological advances, human lives are changing dramatically. The lifestyle changes are particularly reflected in the population's different needs, demands, and new habits. The need for mobility is one of the requirements in this context, which poses a challenging and complex task for transport experts, urban planners, and other professionals. Sustainable mobility is one of the prerequisites for an efficient economy and society. Establishing sustainable mobility is complex because it requires rethinking at the individual level, i.e., at the level of the user - the passenger. The transition from individual transport to sustainable modes remains a complex behavioural process at the individual level and a challenge for society as a whole. Since passengers need to get from one place to another smoothly, quickly, and easily, it is necessary to provide a solution that can compete with the (still predominant) personal vehicle. One of the potential opportunities for efficient and attractive public transport hides behind the concept of multimodal travel. The idea of multimodal travel is to connect the origin and the destination in a given time frame by combining available means of transport to reduce personal vehicle use. In this sense, multimodal trip planning is becoming more important and complex with the emergence of new mobility services (e.g., Mobility-as-a-Service - MaaS) and increasingly sophisticated passenger needs. The EU transport policy highlights the concept of door-to-door multimodal transport as one of the priority measures for alleviating traffic problems. The European Commission has developed a series of legislative and policy initiatives for better, adequate infrastructure and digital solutions to promote an integrated, multimodal, and sustainable transport system. Available travel information can influence travellers' decisions to reach their destination as best as they can. Travellers need advanced information systems (tailored to them) that can provide real-time information about the combined modes before and during the trip. Ad hoc trips (single, one-time trips) are also of great interest to researchers studying traveller mobility. Ad hoc trips are specific purpose trips where the traveller does not know the trip characteristics (such as possible routes, available modes and their combination, travel time, number of transfers, and waiting time when transferring) in advance. Multimodal Journey Planners (MJPs) are a part of Intelligent Transport Systems (ITS) that provide a range of passenger transport services. The basic task of the system is to answer the user's question: "How do I get from place A to place B at a particular departure or arrival time, and under what conditions?" There are several active multimodal journey planner systems in the EU with different functionalities. Most of them include the basic functionalities (criteria for selecting a multimodal route). There is an obvious need for a user-friendly travel information platform. The modern user expects much more than just a route planning app, e.g., selecting a more environmentally friendly route, displaying the weather and road conditions during the journey, and specific personalized settings. The existing MJPs are typically based on a solution that applies the shortest route and travel time criteria. The proposed travel scenarios (using only these two criteria) have proven inadequate. In recent years, special attention has been paid to understanding passenger behaviour, i.e., individual travel patterns, perceptions, and propensities toward multimodal mobility. Traveller behaviour should be perceived as a complex behaviour that involves individuals' perceptions, attitudes, opinions, feelings, beliefs, and intentions. It can be understood as a decision-making process based on accumulated experience and behaviour patterns developed during previous travels. Traveller behaviour can be represented by a "genome model," i.e., a complete set of all biological, motivational, cognitive, and situational information that drives the individual traveller's behaviour. Variability is another feature of passenger behaviour, especially in a dynamic urban environment. The complexity of building such models that enable personalized travel decisions is due to the diversity of traveller preferences. This research aimed to develop an effective model for multimodal travel selection based on multicriteria decision methods. The analysis of the individual methods of multicriteria optimization (the analysis of advantages and disadvantages) and testing on real examples revealed that the model based on the Weighted Sum Method (WSM), including its fuzzy version, is most appropriate for multimodal travel choice. This method provides a good structuring of the observed problem by explicitly describing the alternatives, criteria, relative results, and weights. It is noteworthy that it does not impose any requirements on the decision maker (passenger) and is easy to implement. Moreover, an innovative transformation of the Weighted Product Method (WPM) mathematical model into one very similar in structure to the WSM is proposed enabling the same application of procedures and a similar identification of passenger preferences as in the WSM. The advantage of the transformed WPM over the WSM is that the input data of the alternatives matrix do not need to be pre-normalized. The second part of the research defines the methodology for identifying passengers' multimodal travel preferences based on the recorded selection of individual multimodal trips. Based on previous experience from similar research, the values of the decision criteria obtained after the completion of the activities (trips) most accurately describe travellers' preferences. In addition, it was found that it is best to identify the preferences of each user individually, as opposed to certain approaches based on the preferences of the user's population group (according to particular criteria). This study adopted an approach based on identifying the values of the criteria relating to particular preferences using a procedure based on the so-called inverse model of the decision-making process. The mathematical model of the inverse decision process is defined as the optimization task of determining the vector of criteria weights by minimizing the p-th norm of the approximation error of the decision model. When a quadratic norm is chosen, the problem reduces to the classical least squares error method in matrix form. The conducted tests and validation have returned very good results for the selected method of identifying passengers' multimodal travel preferences.