  We selected 48 European cities and gathered their public transport timetables
in the GTFS format. We utilized Uber's H3 spatial index to divide each city
into hexagonal micro-regions. Based on the timetables data we created certain
features describing the quantity and variety of public transport availability
in each region. Next, we trained an auto-associative deep neural network to
embed each of the regions. Having such prepared representations, we then used a
hierarchical clustering approach to identify similar regions. To do so, we
utilized an agglomerative clustering algorithm with a euclidean distance
between regions and Ward's method to minimize in-cluster variance. Finally, we
analyzed the obtained clusters at different levels to identify some number of
clusters that qualitatively describe public transport availability. We showed
that our typology matches the characteristics of analyzed cities and allows
succesful searching for areas with similar public transport schedule
characteristics.
