The following document presents an analysis of shared, dockless electric scooter systems in several American cities and a web tool for predicting scooter demand in cities that do not currently have shared scooters. We focus on the equity implications of these systems: who currently has access to scooters, and who will have access if we keep following the business-as-usual approach? This document presents an overview of our data and use case, a summary and key takeaways from our analysis, and an appendix with all of the R code used in the project.
This project was produced for the MUSA/Smart Cities Practicum course (MUSA 801) taught by Ken Steif, Michael Fichman, and Matt Harris in the Master of Urban Spatial Analytics and Master of City Planning Programs at the University of Pennsylvania. We are deeply grateful to our instructors for their guidance, feedback, and attention throughout the semester, despite the challenges brought on by the ongoing pandemic. We also thank Michael Schnuerle from the City of Louisville Metro Government and Sharada Strasmore from the DC Department of Transportation for providing data that made our rebalancing analysis possible as well as sharing their insights into and knowledge of the scooter and micromobility planning process. Lastly, we would like to acknowledge our classmates in MUSA and city planning, who not only produced incredible projects of their own this semester, but also provided thoughtful feedback and support throughout our time in the programs.
In the few short years since they first launched, shared, dockless electric scooters have become ubiquitous sights on streets and sidewalks in cities across America. What may have first been seen as novelties or purely recreational vehicles now play critical roles in many people’s daily transportation routines. Despite being relative newcomers to the urban transportation scene, dockless scooters provided over 38 million trips in 2018, more than the number of rides taken on traditional station-based bikeshare systems that year. Yet, despite these vehicles having enmeshed themselves quickly in the urban fabric, access to electric scooters is not spread equitably across cities. While residents in wealthier, predominantly white downtown neighborhoods enjoy easy access to shared scooters, residents in poorer but comparably dense parts of cities outside of downtown are underserved by the systems.
In this study, we use a combination of open and private dockless scooter usage data from six American cities to construct a model for predicting ridership in ten cities that have not had scooter share systems in the past. While our model displayed sizable errors, showing that it requires further calibrating, it also suggests that the business-as-usual approach to introducing scooters into a new market is likely to create inequitable access to the vehicles for residents. While cities such as Louisville, KY have recognized these inequities and instituted distribution requirements to address them, we show through analysis of vehicle rebalancing data that providers do not seem to be complying with these requirements, and stronger enforcement may be necessary. Lastly, we introduce a proof-of-concept web application that allows users to explore the spatial distribution of our model’s predictions for each city and compare them to demographic and socioeconomic variables of interest. We believe that this tool will allow policymakers to anticipate the geography of scooter ridership in their cities and understand - and ultimately plan for - the inequities that may be created by the business-as-usual approach to launching and administering scooter share systems.