How Can Bikesharing Systems Benefit Transit Service in Beijing? A Data-Driven Approach

This research aims to explore the areas where transit service can be improved through analyzing the use of dockless bikesharing systems. It initially introduces the overall characteristics of bike use in Beijing and finds that weekday cycling shows a certain regularity and travel distributions of the hot riding area at different periods are revealed to have little difference. Next, each trip purpose is investigated and categorized by using density-based spatial clustering of applications with noise (DBSCAN) analysis to divide bikesharing origins and destinations into five types based on different built environment features. Results show that most riders prefer to transfer to other public transit systems. On the basis of distinct transit travel mode choice data obtained from mobile navigation software, including expected travel time, walking distance, and travel cost, an evaluation index model is formulated to measure and evaluate transit performance for network optimization. The proposed framework can help local transit authorities make targeted countermeasures that improve service quality and attract more passengers.