  The role of spatial data in tackling city-related tasks has been growing in
recent years. To use them in machine learning models, it is often necessary to
transform them into a vector representation, which has led to the development
in the field of spatial data representation learning. There is also a growing
variety of spatial data types for which representation learning methods are
proposed. Public transport timetables have so far not been used in the task of
learning representations of regions in a city. In this work, a method is
developed to embed public transport availability information into vector space.
To conduct experiments on its application, public transport timetables were
collected from 48 European cities. Using the H3 spatial indexing method, they
were divided into micro-regions. A method was also proposed to identify regions
with similar characteristics of public transport offers. On its basis, a
multi-level typology of public transport offers in the regions was defined.
This thesis shows that the proposed representation method makes it possible to
identify micro-regions with similar public transport characteristics between
the cities, and can be used to evaluate the quality of public transport
available in a city.
