  Public transport systems are expected to reduce pollution and contribute to
sustainable development. However, disruptions in public transport such as
delays may negatively affect mobility choices. To quantify delays, aggregated
data from vehicle locations systems are frequently used. However, delays
observed at individual stops are caused inter alia by fluctuations in running
times and propagation of delays occurring in other locations. Hence, in this
work, we propose both the method detecting significant delays and reference
architecture, relying on stream processing engines, in which the method is
implemented. The method can complement the calculation of delays defined as
deviation from schedules. This provides both online rather than batch
identification of significant and repetitive delays, and resilience to the
limited quality of location data. The method we propose can be used with
different change detectors, such as ADWIN, applied to location data stream
shuffled to individual edges of a transport graph. It can detect in an online
manner at which edges statistically significant delays are observed and at
which edges delays arise and are reduced. Detections can be used to model
mobility choices and quantify the impact of repetitive rather than random
disruptions on feasible trips with multimodal trip modelling engines. The
evaluation performed with the public transport data of over 2000 vehicles
confirms the merits of the method and reveals that a limited-size subgraph of a
transport system graph causes statistically significant delays
