Pattern Recognition in Complex Activity Travel Patterns: Comparison of Euclidean Distance, Signal-Processing Theoretical, and Multidimensional Sequence Alignment Methods

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Abstract
The application of a multidimensional sequence alignment method for classifying activity travel patterns is reported. The method was developed as an alternative to the existing classification methods suggested in the transportation literature. The relevance of the multidimensional sequence alignment method is derived from the fact that structural information (both interdependency and sequential relationships) embedded in activity travel patterns is taken into account—a property not shared with existing classification methods. The performance of the multidimensional sequence alignment method is compared with several other methods (Euclidean distance and signal processing) that have been used in activity analysis in the past.

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