Revealing correlation patterns of individual location activity motifs between workdays and day-offs using massive mobile phone data

Abstract

Recently, people are increasingly interested in understanding broader and longer-term individual activity-travel behaviors across days or even weeks. However, the relationship of individual daily activity-travel behaviors over multi-days only remains partially revealed, especially between workdays and day-offs. Thus, we develop an effective framework to extract individual daily activity-travel patterns from massive mobile phone network data on basis of location activity motifs (LAMs), which are beneficial to combining the locations, activities, and trips in daily activity-travel behaviors. We then discover that the modified number of LAMs over time conforms well to the classic exploration and preferential return (EPR) model after excluding the influence of the number of trips, which reproduces the human activity-travel behavior characteristics on daily scale and indicates that the complex relationship of LAMs exists between workdays and day-offs. Furthermore, we emphasize three categories of correlation patterns while the relationship of LAMs between workdays and day-offs is instantiated using association rules mining algorithm. Ultimately, the regular individual differences and obvious spatial heterogeneity reveal the formation mechanism of correlation patterns. These empirical results contribute to develop different but related transportation strategies between workdays and day-offs by understanding individual daily activity-travel behaviors.