Similarity analysis of frequent sequential activity pattern mining

Activity pattern classification is extensively studied using multi-person single-day mobile traces. However, human mobility exhibits intra-personal variability and thus single-day activity may not fully capture one's activity patterns. This paper creates a methodological framework to analyze similarity of activity patterns using frequent sequential pattern mining when multi-person multi day data is available. Frequent sequential pattern mining discovers the frequently occurring ordered subsequences and is a natural approach of analyzing multi-day travel patterns. Prefix Span algorithm is implemented to extract frequent patterns for each individual. Then similarity measures are defined to describe the extent to which two travelers' activity patterns are alike and the regularity of how one repeats her activity patterns from day to day. Based upon the pairwise similarity values between two individuals, hierarchical clustering is conducted to divide travelers into communities. To illustrate these methodologies, 349 travelers' 19,130 travel activity sequences are extracted from the world's first connected vehicle testbed in Ann Arbor, Michigan. Three major clusters are identified. Coupled with demographics, these clusters are characterized as "seniors", "working class", and "parents", respectively. Multinomial logistic regression is employed to model to what extent the similarity of socio-demographics can explain that of travel patterns. This work can be extended to either infer an unknown user's demographics (or customer profiling) based on her activity patterns, or to reconstruct an unknown user's frequent activity patterns based on her demographics and other similar travelers' patterns.