Exploratory Analysis of Real-Time E-Scooter Trip Data in Washington, D.C.

Abstract

The proliferation of micromobility, evolving from station-based to dockless bikeshare programs, has dramatically accelerated since 2017 with an influx of investment from the private sector to a new product, dockless e-scooter share. As an alternative to pedal bikes, e-scooters have become widespread across the U.S.A. owing to the unprecedented convenience they bring to commuters and travelers with electric-power propulsion and freedom from docking stations. In cities like Washington, D.C., e-scooter share can play an important role to support transportation sustainability and boost accessibility in less-connected communities. This study takes advantage of publicly available but not readily accessible e-scooter share data in Washington, D.C. for an initial view of the travel patterns and behaviors related to this new mode. The study adopted an innovative approach to scrape and process general bikeshare feed specification data in real time for e-scooters. Not only locational time series data, but also e-scooter share trip trajectories were generated. The trip trajectory data provide a unique opportunity to examine travel patterns at the street link level—a level of analysis that has not been reached before for e-scooter share to the authors’ knowledge. The paper first provides descriptive statistics on e-scooter share trips, followed by an exploratory analysis of trip trajectories conjoined with street link level features. Important insights on e-scooter route choice are derived. Lastly, policy and regulatory implications in relation to e-scooter facility design and safety risks are discussed.