Clustering of Activity Patterns Using Genetic Algorithms

Abstract

Finding groups of individuals with similar activity patterns (a sequence of activities within a given time period, usually 24 hours) has become an important issue in models of activity-based approaches to travel demand analysis. This knowledge is critical to many activity-based models, and it aids our understanding of activity/travel behavior. This paper aims to develop a methodology for the clustering of these patterns. There is a large number of well-known clustering algorithms, such as hierarchical clustering, or k-means clustering (which belong to the class of partitioning algorithm). However, these algorithms cannot be used to cluster categorical data, so they do not suit the problem of clustering of activity patterns well. Several other heuristics have been developed to overcome this