Micromobility Trip Origin and Destination Inference Using General Bikeshare Feed Specification Data

Emerging micromobility services (e.g., e-scooters) have a great potential to enhance urban mobility but more knowledge on their usage patterns is needed. The General Bikeshare Feed Specification (GBFS) data are a possible source for examining micromobility trip patterns, but efforts are needed to infer trips from the GBFS data. Existing trip inference methods are usually based on the assumption that the vehicle identity (ID) of a micromobility option (e-scooter or e-bike) does not change, and so they cannot deal with data with vehicle IDs that change over time. This paper proposes a comprehensive package of algorithms to infer origin-destination (OD) pairs from GBFS data with static vehicle ID and unlinked trip origins and destinations from GBFS data with resetting and dynamic vehicle ID. The algorithms were implemented in Washington D.C. by analyzing one week (last week of February 2020) of GBFS data published by six vendors, and the inference accuracy of the proposed algorithms are evaluated by R-squared, mean absolute error, and sum absolute error. It is found that the R-squared measure is larger than 0.9 and the MAE measure is less than 2 when the algorithms are evaluated with a 400 m x 400 m grid. The absolute errors are relatively larger in the downtown area, and the inference error is relatively high during early morning and early nighttime. The accuracy of the trip inference algorithms is sufficiently high for most practical applications.