Identifying individual anchoring regions by mining public transport smart card data

Abstract Understanding locations significant to an individual’s routine travels – such as home and work – gives important context to the individual’s travel patterns. Termed 'anchoring points', these represent places where activities are repeated in space and time around which other activities are arranged. While data from public transport automated fare collection (AFC) systems provides a structured and detailed view of movement patterns with minimal data collection efforts, it cannot provide the same level of background detail of these locations that can be achieved with more labour-intensive data collection methods such as user travel surveys. Where previous studies have developed methods based on AFC data to identify these anchoring points for each individual as points, this work considers these locations as spatial regions 'anchoring regions', acknowledging that individuals may have multiple transport options available to them to access the same activities. Further, this work also develops an approach for allowing individuals to have a flexible number of these anchoring regions, recognising that individual routines and circumstances may differ greatly. Using temporal patterns (such as time of day and duration of time spent at the location) to identify likely activity labels and then the distribution of these activities within each region, a Gaussian mixture model is used to characterise these regions. This resulted in five region activity clusters, with the activity distributions compared to land use and the final clusters of type: 'residences', 'workplaces', 'education', 'residences/leisure', 'workplaces/leisure' . While a sample of the full dataset was used for clustering, the stability of the resulting region activity clusters was tested with a newly developed methodology to validate the results across sample sizes.