Mining bike sharing trip record data: a closer examination of the operating performance at station level

Bike sharing systems gain traction worldwide, but previous research pay less attention to the more detailed operating characteristics at station level. This study aims to fill this void in the literature by looking into the stations’ performance with considering systemic intervention and user-driven usage. Methodologically, an innovative approach that captures the underlying relevance of trip records is proposed firstly to identify the bicycle-based operating states in its lifecycle, such as being redistributed, parked, or used. From bike to station, all the bicycle-based operating status information can be linked to associated stations, consequently, station vitality and station pattern are refined into stations’ operating performance. In addition to rational classification and discussion of operating features, this study has explored the impact of surrounding built environment on these specific operating features instead of simple trip intensity. To test the proposed methodology, trip record data from the bike sharing system of Boston in 2019 is used. The results indicate that user-driven and manual-scheduling bike movements are all particularly relevant to keeping stations’ sustainable daily operation, but vary across the stations in their ratio. In terms of station vitality and station pattern, some stations would embody the nature of high-output-scheduling, low-bike-turnover, or high-input-scheduling relative to the baseline scenario of operating performance. Heterogeneity of stations in operating is also proved to be caused by the surrounding built environment. The outcomes and methodological framework would facilitate the assessment of bike sharing system operating state at station level, as well as instilling new insights into bike sharing system design.