Clustering of Activity Patterns Using Genetic Algorithms Ond˘ rej P˘ ribyl Pennsylvania Transportation Institute & Department of Civil & Environmental Engineering Pennsylvania State University 201 Transportation Research Bldg. University Park, PA 16802 oxp112@psu.edu Abstract Finding groups of individuals with similar activity patterns (a sequence of ac- tivities 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 under- standing of activity/travel behavior. This paper aims to develop a methodol- ogy 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 belongs to the class of partitioning algorithm). However, these algo- rithms cannot be used to cluster categorical data, so they do not suit the prob- lem of clustering of activity patterns well. Several other heuristics have been developed to overcome this problem. The k-medoids algorithm, described in this paper, is a modification of the k-means algorithm with respect to categori- cal data. However, similar to the k-means algorithm, the k-medoids algorithm can converge to local optima. This paper approaches the medoids-based for- mulation of clustering problem using genetic algorithms (GAs), a probabilistic search algorithm that simulates natural evolution. The main objective of this paper is to develop a robust algorithm that suits the problem of clustering of activity patterns and to demonstrate and discuss its properties. 1 Introduction Finding groups of individuals with similar activity patterns (a sequence of activities within a given time period, usually 24 hours) has become an impor- tant issue in models of activity-based approaches to travel demand analysis.