  The fast-growing amount of traffic data brings many opportunities for
revealing more insightful information about traffic dynamics. However, it also
demands an effective database management system in which information retrieval
is arguably an important feature. The ability to locate similar patterns in big
datasets potentially paves the way for further valuable analyses in traffic
management. This paper proposes a content-based retrieval system for
spatiotemporal patterns of highway traffic congestion. There are two main
components in our framework, namely pattern representation and similarity
measurement. To effectively interpret retrieval outcomes, the paper proposes a
graph-based approach (relation-graph) for the former component, in which
fundamental traffic phenomena are encoded as nodes and their spatiotemporal
relationships as edges. In the latter component, the similarities between
congestion patterns are customizable with various aspects according to user
expectations. We evaluated the proposed framework by applying it to a dataset
of hundreds of patterns with various complexities (temporally and spatially).
The example queries indicate the effectiveness of the proposed method, i.e. the
obtained patterns present similar traffic phenomena as in the given examples.
In addition, the success of the proposed approach directly derives a new
opportunity for semantic retrieval, in which expected patterns are described by
adopting the relation-graph notion to associate fundamental traffic phenomena.
