The department is currently coordinating the establishment of the CTU Competence Center for Cooperative and Autonomous Mobility. The area of autonomous mobility includes various technical topics and technologies that enable transportation vehicles and systems to operate without human control or with minimal human intervention. This includes the autonomous control of the vehicle itself, the management of the operation of these vehicles, and their integration into city management, strategic activities in planning autonomous mobility and supporting its development, the connection to other modes of transportation and other participants (cycling, scooters, vulnerable road users), and last but not least, the testing and analysis of the impact of autonomous technologies (in a simulation environment or on test tracks) and the analysis of collected data.
One of the important topics that we focus on in the department is data processing. In many industries, information is collected through questionnaires. Once the completed questionnaires have been collected, the data they contain needs to be analysed based on the model developed. Since the questionnaires yield discrete data (a finite number of possible responses to a question), a discrete (categorical) model comes into consideration. However, this has the disadvantage that its dimensionality for a larger number of variables (queries) and a larger number of possible answers grows unbearably. For example, for 10 queries and 10 possible answers to each query, the dimension of the model is ten to the tenth, that is, 10,000,000. Our effort is to circumvent this drawback by reducing the dimension.
In other situations, we need to process data and signals from detectors of various kinds.
In our department, floppy logic was developed, which is exceptional in that it was able to successfully solve the problem of connecting logic with probability theory while keeping the laws of two-valued logic. The theory is simple and very elegant - learn more at floppylogic.cz/.
For more than a quarter of a century, the history of mathematics has been one of the significant research fields of our department. We participate in the organization of national and international conferences, have worked on numerous grants including projects supported by the GA ČR agency, have published numerous publications, and are pleased with the positive feedback and awards received.
Image recognition, deep learning and data analysis are revolutionising transport and traffic systems. These advanced technologies enable the processing and analysis of vast amounts of not only visual data in real time, resulting in significant improvements in safety, efficiency and user-friendliness in transport. Experts from the Department of Applied Mathematics are working on these issues in their research and student projects, which are the basis for final bachelor, master and doctoral theses.
Travel behaviour modelling is the study of how people plan and execute their journeys. It examines factors such as transport mode choice, route, time, and motivation, which helps to optimise transport systems and make urban mobility more efficient and attractive. It uses data and analytical methods to understand travellers' preferences and decision-making, enabling better planning of infrastructure and transport services.
We deal with transport modelling using various types of programmes (Eclipse SUMO, MATSim, AnyLogic, etc.) and solving relevant optimisation tasks. The models allow, for example, testing different scenarios and transport measures in the framework of spatial planning, optimization of planned infrastructure for clean mobility, modelling of environmental impacts, etc.