  In recent years many studies of urban mobility based on large data sets have
been published: most of them based on crowdsourced GPS data or smart-card data.
We present, what is to our knowledge the first, exploration of public transport
delay data harvested from a large-scale, official public transport positioning
system, provided by the Wroc{\l}aw Municipality. We evaluate the
characteristics of delays between stops in relation to direction, time and
delay variance of 1648 stop pairs from 15 mln delay reports. We construct a
normalized feature matrix of likelihood of a given delay change happening at a
given hour on the edge between two stops. We then calculate distances between
such matrices using earth mover's distance and cluster them using hierarchical
agglomerative clustering with Ward's linkage method. We obtain four profiles of
delay changes in Wroc{\l}aw: edges without impact on delay, edges likely to
cause delay, edges likely to decrease delay and edges likely to strongly
decrease delay (ex. when a public transport vehicle is speeding). We analyze
the spatial and mode of transport properties of each cluster and provide
insights into reasons of delay change patterns in each of the detected
profiles.
