Unravelling individual mobility temporal patterns using longitudinal smart card data

The increasing availability of longitudinal individual human mobility traces enables the disaggregate analysis of temporal properties of mobility patterns. The objective of this study is to identify distinctive market segments in terms of habitual temporal travel patterns of public transport users. First, travel patterns are clustered using a Kmeans approach followed by grouping the resulting patterns into a small number of profiles using a hierarchical clustering method. Second, we construct user-week vectors that are then clustered using a Gaussian Mixture Model approach. We apply our clustering analysis to the multi-modal public transport system of Stockholm County, Sweden, using data from more than 3 million smart card-holders. Our clustering analysis resulted in 10 day-of-the-week patterns with their composition varying across the county. In addition, we identify the following hour-by-hour weekly profiles:'Weekly commuters', 'Lower peaks','Late travellers', 'Early birds' and 'Flat curve'. The behavior represented by 'Weekday commuters' and 'Lower peaks' is most persistent over weeks. We demonstrate how a better understanding of user travel patterns offers policy makers, service planners and providers with enhanced opportunities to understand and cater for diverse market segments, for example by means of tailored fare products.