Shared E-Scooter Service Providers with Large Fleet Size Have a Competitive Advantage: Findings from E-Scooter Demand and Supply Analysis of Nashville, Tennessee

Abstract

Shared e-scooter systems are one of the fastest-growing micromobility modes in the United States. In response to service providers’ rapid deployment of e-scooter vehicles, several city governments have regulated shared e-scooters through permits and pilot programs, including the number of service providers, their fleet size, and provisions for expanding/downsizing the fleet size. However, the literature lacks an empirical analysis of the demand elasticity of shared e-scooters. We used a negative binomial fixed effect regression to evaluate the demand elasticity of e-scooter vehicle deployment using the Shared Urban Mobility Device (SUMD) dataset from Nashville, Tennessee, between April 2019 and February 2020. This dataset included disaggregated e-scooter trip summary data and vehicle location data that updates approximately every five minutes. We also estimated land-use specific demand elasticity of e-scooter vehicle deployment by clustering Traffic Analysis Zones (TAZs) using the K-means algorithm. We found that the average daily demand elasticity of e-scooter vehicle deployment is inelastic (0.55). Service providers with large fleet sizes (>500) have a demand elasticity of e-scooter deployment that is 2.5 times higher than that of medium fleet-sized service providers (250-500). We also found a significant difference in demand elasticity of e-scooter deployment for land use types, with university and park & waterfront land uses having the highest elasticity values. These findings could be helpful for city governments to identify the optimal number of service providers and fleet sizes to permit so that demand is fulfilled without an oversupply of e-scooter vehicles in public spaces.

Keywords: shared e-scooters, elasticity, Micromobility, demand, supply