Activity pattern similarity: a multidimensional sequence alignment method

Abstract

The classification of activity patterns is an important research topic in activity analysis. First, it constitutes the basis for analyzing activity patterns, for instance by correlating the derived classification with spatial and/or socio-economic variables. Secondly, the underlying mechanisms can be used to assess the degree of correspondence between observed activity patterns and activity patterns predicted by some activity-based model of transport demand. Traditionally, conventional Euclidean distance measures have been used for the comparison of activity patterns. Consequently, the sequence information embedded in activity patterns has not been explicitly considered when comparing activity patterns. More recently, sequence alignment methods have been proposed. Although these methods have some advantages, they are uni-dimensional and hence cannot incorporate the interdependencies between attributes. This paper therefore proposes a multidimensional sequence alignment method to measure differences in both sequential and interdependency information embedded in activity patterns.