Spatial trajectories segmentation: trends and challenges

Abstract

Given a sequence S of temporally ordered observations, non necessarily of spatial nature, the segmentation task partitions S in a set of disjoint sub-sequences si, .., sn - the segments - such that ∪i∈[1, n] si = S. Typically, segments represents sub-sequences that are somehow homogeneous with respect to some criteria. Depending on the context and the nature of observations, segments can be given an approximated representation, for example segments can be assigned a descriptive label or one of the data points is chosen as representative of the whole sub-sequence. The final result is a summarized representation of the sequence. This simple and intuitive mechanism has been extensively studied in literature, for example, for the summarization of time series. Interestingly, the notion of segment is also at the basis of the most recent trajectory data models. For example, segments are the informative units in the semantic trajectories, where they are called episodes. Episodes are spatial sub-trajectories that can be semantically annotated using application-dependent descriptions, e.g. place names [1]. Similarly the recent symbolic trajectory data model [2] describes the individual movement as a sequence of temporally annotated labeled states s1, ..sn, where each state si is associated with a time interval. Beyond data modeling, segmentation can be employed for the indexing of trajectories in moving object databases while another major role is to support data analysis, especially for the extraction of individual mobility patterns. The concept of trajectory segment is thus emerging as shared and perhaps unifying concept across data modeling, indexing and analysis.