  The discovery of spatio-temporal dependencies within urban road networks that
cause Recurrent Congestion (RC) patterns is crucial for numerous real-world
applications, including urban planning and scheduling of public transportation
services. While most existing studies investigate temporal patterns of RC
phenomena, the influence of the road network topology on RC is often
overlooked. This article proposes the ST-Discovery algorithm, a novel
unsupervised spatio-temporal data mining algorithm that facilitates the
effective data-driven discovery of RC dependencies induced by the road network
topology using real-world traffic data. We factor out regularly reoccurring
traffic phenomena, such as rush hours, mainly induced by the daytime, by
modelling and systematically exploiting temporal traffic load outliers. We
present an algorithm that first constructs connected subgraphs of the road
network based on the traffic speed outliers. Second, the algorithm identifies
pairs of subgraphs that indicate spatio-temporal correlations in their traffic
load behaviour to identify topological dependencies within the road network.
Finally, we rank the identified subgraph pairs based on the dependency score
determined by our algorithm. Our experimental results demonstrate that
ST-Discovery can effectively reveal topological dependencies in urban road
networks.
