Forecasting e-scooter substitution of direct and access trips by mode and distance

TL;DR

This paper develops a predictive model for e-scooter trips in U.S. cities, estimating substitution patterns for various transportation modes and analyzing potential revenue impacts of scooter deployment.

Contribution

It introduces a novel nonlinear, multifactor model to estimate e-scooter trip substitution across multiple transportation modes based on statistical similarity.

Findings

E-scooters could replace 32% of carpool trips.

E-scooters could substitute 13% of bike trips.

Estimated annual revenue from 2000 scooters in Manhattan is 77 million USD.

Abstract

An e-scooter trip model is estimated from four U.S. cities: Portland, Austin, Chicago and New York City. A log-log regression model is estimated for e-scooter trips based on user age, population, land area, and the number of scooters. The model predicts 75K daily e-scooter trips in Manhattan for a deployment of 2000 scooters, which translates to 77 million USD in annual revenue. We propose a novel nonlinear, multifactor model to break down the number of daily trips by the alternative modes of transportation that they would likely substitute based on statistical similarity. The model parameters reveal a relationship with direct trips of bike, walk, carpool, automobile and taxi as well as access/egress trips with public transit in Manhattan. Our model estimates that e-scooters could replace 32% of carpool; 13% of bike; and 7.2% of taxi trips. The distance structure of revenue from access/egress trips is found to differ from that of other substituted trips.