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Managing the frequent traffic congestion (traffic jams) of the road networks of large cities is a major challenge for municipal traffic management organizations. In order to manage these situations, it is crucial to understand the processes that lead to congestion and propagation, because the occurrence of a traffic jam does not merely paralyze one street or road, but could spill over onto the whole vicinity (even an entire neighborhood). Solutions can be found in professional literature, but they either oversimplify the problem, or fail to provide a scalable solution. In this article, we describe a new method that not only provides an accurate road network model, but is also a scalable solution for identifying the direction of traffic congestion propagation. Our method was subjected to a detailed performance analysis, which was based on real road network data. According to testing, our method outperforms the ones that have been used to date.

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The road network of big cities is a complex and hardly analyzable system in which the accurate quantification of interactions between nonadjacent road segments is a serious challenge. In this paper we would like to present a novel method able to determine the effects (the time delay and the level of the correlation) of distinct road segments on each other of a smart city's road network. To reveal these relationships, we are investigating unexpected events such as traffic jams or accidents. This novel analysis can give a significant insight to improve the operation of currently widespread traffic prediction algorithms.