Recent times have seen a shift from traditional docked to dockless bike-sharing systems. It is popular among consumers as it allows flexibility to drop off bikes anywhere and solves the last-mile problem of transportation. While convenient for users, the dockless bike-share system’s free-floating model introduces the problem of hyperlocal imbalance, about which little or no research is available. The hyperlocal imbalance is the supply-demand disparity created in a small geographical region due to consumer’s bias to pick up bikes from some locations compared to others. This paper introduces, demonstrates, and determines the reasons behind the hyperlocal-imbalance in dockless-bike-sharing systems.

The study of hyperlocal imbalance requires access to fine-grained trip-level data, which is not easily accessible to the research community due to privacy or competition issues. To deal with it, in this work, we introduce an algorithm to extracttrip-level information from the General Bikeshare Feed Specification (GBFS) feeds, which bike-share companies are obliged to upload as per transportation department regulations across the US. The algorithm is validated against the actual trip data of dockless bikes. It extracts the trip details from the GBFS data with a recall of 77% and precision of 80%.