  Novel forms of data analysis methods have emerged as a significant research
direction in the transportation domain. These methods can potentially help to
improve our understanding of the dynamic flows of vehicles, people, and goods.
Understanding these dynamics has economic and social consequences, which can
improve the quality of life locally or worldwide. Aiming at this objective, a
significant amount of research has focused on clustering moving objects to
address problems in many domains, including the transportation, health, and
environment. However, previous research has not investigated the lifecycle of a
cluster, including cluster genesis, existence, and disappearance. The
representation and analysis of cluster lifecycles can create novel avenues for
research, result in new insights for analyses, and allow unique forms of
prediction. This technical report focuses on studying the lifecycle of clusters
by investigating the relations that a cluster has with moving elements and
other clusters. This technical report also proposes a big data framework that
manages the identification and processing of a cluster lifecycle. The ongoing
research approach will lead to new ways to perform cluster analysis and advance
the state of the art by leading to new insights related to cluster lifecycle.
These results can have a significant impact on transport industry data science
applications in a wide variety of areas, including congestion management,
resource optimization, and hotspot management.
