Sequence alignment, also known as optimal matching, has recently received new attention for use in analysis of activity patterns. The method is almost always combined with a data reduction technique, such as clustering analysis. The cluster-based approach is powerful for discovery of a typology of activity patterns of people. However, the use of the combination of the sequence alignment and cluster analysis methodologies does not seem to be successful for the identification of diverse factors that would affect activity sequence patterns. This outcome is because the loss of too much information may occur when the set of activity sequences is reduced to a small number of clusters. This paper proposes the use of a new combination of the sequence alignment and discrepancy analysis methodologies instead of the cluster-based approach. As a generalization of the principle of analysis of variance, discrepancy analysis allows the association between activity sequences characterized by a pairwise distance matrix and one or more covariates to be evaluated. In addition, an induction tree complements the sequence discrepancy analysis and displays how individual activity sequences vary with the value of covariates.
Discrepancy Analysis of Activity Sequences
What Explains the Complexity of People's Daily Activity–Travel Patterns?
Transportation Research Record: Journal of the Transportation Research Board
Transportation Research Record: Journal of the Transportation Research Board ; 2413 , 1 ; 24-33
2014-01-01
Article (Journal)
Electronic Resource
English
Autonomous vehicle model training and validation using low-discrepancy sequences
European Patent Office | 2024
|Improvement of the Performances of Genetic Algorithms by Using Low-discrepancy Sequences
British Library Online Contents | 2006
|Vehicle environment discrepancy identification and discrepancy distribution to other vehicles
European Patent Office | 2024
|