  The rise of dockless bike-sharing systems has led to increased interest in
using bike-sharing data for urban transportation and travel behavior research.
However, few studies have focused on the individual daily mobility patterns,
hindering their alignment with the increasingly refined needs of urban active
transportation planning. To bridge this gap, this study presents a two-layer
framework, integrating improved flow clustering methods and multiple rule-based
decision trees, to mine individual cyclists' daily home-work commuting patterns
from vast dockless bike-sharing trip data with users' IDs. The effectiveness
and applicability of the framework is demonstrated by over 200 million dockless
bike-sharing trip records in Shenzhen. Ultimately, based on the mining results,
we obtain two categories of bike-sharing commuters (i.e., 74.38% of Only-biking
commuters and 25.62% of Biking-with-transit commuters) and some interesting
findings about their daily commuting patterns. For instance, lots of
bike-sharing commuters live near urban villages and old communities with lower
costs of living, especially in the central city. Only-biking commuters have a
higher proportion of overtime than Biking-with-transit commuters, and the
Longhua Industrial Park, a manufacturing-oriented area, having the longest
average working hours (over 10 hours per day). Massive commuters utilize
bike-sharing for commuting to work more frequently than for returning home,
which is closely related to the over-demand for bike-sharing around workplaces
during commuting peak. Overall, this framework offers a cost-effective way to
understand residents' non-motorized mobility patterns. Moreover, it paves the
way for subsequent research on fine-scale cycling behaviors that consider
demographic disparities in socio-economic attributes.
