  Existing studies have extensively used spatiotemporal data to discover the
mobility patterns of various types of travellers. Smart card data (SCD)
collected by the automated fare collection systems can reflect a general view
of the mobility pattern of public transit riders. Mobility patterns of transit
riders are temporally and spatially dynamic, and therefore difficult to
measure. However, few existing studies measure both the mobility and stability
of transit riders' travel patterns over a long period of time. To analyse the
long-term changes of transit riders' travel behaviour, the authors define a
metric for measuring the similarity between SCD, in this study. Also an
improved density-based clustering algorithm, simplified smoothed ordering
points to identify the clustering structure (SS-OPTICS), to identify transit
rider clusters is proposed. Compared to the original OPTICS, SS-OPTICS needs
fewer parameters and has better generalisation ability. Further, the generated
clusters are categorized according to their features of regularity and
occasionality. Based on the generated clusters and categories, fine- and
coarse-grained travel pattern transitions of transit riders over four years
from 2010 to 2014 are measured. By combining socioeconomic data of Beijing in
the year of 2010 and 2014, the interdependence between stability and mobility
of transit riders' travel behaviour is also discussed.
