Highlights The study investigate how temporal, land use, transport infrastructure, and weather attributes impact available e-scooter presence and variation in e-scooter presence in Washington D.C. Multi-level mixed effects linear regression models were generated. Temporal effects were present, as weekends and late nights were associated with fewer e-scooters and less variation in hourly e-scooter presence. The average number of e-scooter per geographic unit of analysis available on weekends was 0.26 fewer than on weekdays. The findings can help policy-makers in cities with comparable climates, land use characteristics, and transport infrastructure, make appropriate decisions in recognizing e-scooters as an urban mobility solution.

    Abstract Micromobility, including the use of shared electric scooters (e-scooters), emerged rapidly in North America and is marketed as an alternative to car reliance, especially for short distance travel in urban settings. Our study aims to contribute to our understanding of how shared e-scooters are used by examining the factors that determine the presence of e-scooters, as well as those that cause variation in e-scooter presence between each consecutive hour and throughout the day. The object of this study is to investigate how temporal, land use, transport infrastructure, and weather attributes impact available e-scooter distribution and variation in e-scooter presence in Washington D.C., to reveal use patterns and develop a framework for studying citywide e-scooter systems. Data on the location of e-scooters in the Washington D.C. area over six full days was collected. Then, multilevel mixed effects linear regression models were generated to investigate the impact of time, land use characteristics, and the built environment while controlling for weather conditions. We found that temporal effects were present, as weekends and late nights were associated with fewer e-scooters and less variation in hourly e-scooter presence. We observed that the average number of e-scooters available per 0.07 mile2 on weekends was 0.26 (7.81%) fewer than on weekdays, and 0.82 (24.62%) fewer during the late night than other times of day, all else held constant. Higher population density, density of places of interest, and activities were generally associated with more e-scooters and contributed to more change in the hour-to-hour numbers of e-scooters but less variation throughout the day. Bikeshare stations and bicycle lanes positively impacted presence, they increased the odds of e-scooter presence by 3.16 and 2.73 times respectively and change in the average number of e-scooters nearby. The hourly change in the average numbers of e-scooters near bikeshare stations was 0.19 all else held equal, and it is unclear whether e-scooters were used as first-mile last-mile solutions for public transport. These findings can help policy-makers in cities with comparable climates, land use characteristics, and transport infrastructure. The findings can help city planners and engineers make appropriate decisions in recognizing e-scooters as an urban mobility solution, where to expect them to emerge in different parts of the city, and how e-scooters interact with established transport systems.


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    Title :

    Scoot over: Determinants of shared electric scooter presence in Washington D.C


    Contributors:
    Hawa, Leila (author) / Cui, Boer (author) / Sun, Lijun (author) / El-Geneidy, Ahmed (author)

    Publication date :

    2021-01-10


    Size :

    13 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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