Mining individual daily commuting patterns of dockless bike-sharing users: a two-layer framework integrating spatiotemporal flow clustering and rule-based decision trees
The rise of dockless bike-sharing systems has led to increased interest in using bike-sharing data for sustainable transportation and travel behavior research. However, these studies have rarely focused on the individual daily mobility patterns, hindering their alignment with the increasingly refined needs of active transportation planning. To bridge this gap, this paper 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 dockless bike-sharing trip data with user IDs. The effectiveness and applicability of the framework is demonstrated by over 200 million bike-sharing trip records in Shenzhen. Based on the mining results, we obtain two categories of bike-sharing commuters (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, has the longest average working hours (over 10 hours per day). Moreover, massive users utilize bike-sharing for commuting to work more frequently than for returning home, which is intricately related to the over-demand for bikes around workplaces during commuting peak. In sum, this framework offers a cost-effective way to understand the nuanced non-motorized mobility patterns and low-carbon trip chains of residents. It also offers novel insights for improving the bike-sharing services and planning of active transportation modes.