  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.
