A Stochastic Model for Electric Scooter Systems

43 Pages Posted: 18 May 2020

See all articles by Jamol Pender

Jamol Pender

Cornell University - School of Operations Research and Industrial Engineering

Shuang Tao

Cornell University - Operations Research & Industrial Engineering

Anders Wikum

affiliation not provided to SSRN

Date Written: April 21, 2020

Abstract

Electric scooters are becoming immensely popular across the world as a means of reliable transportation around many cities. As these e-scooters rely on batteries, it is important to understand how many of these e-scooters have enough battery life to transport riders and when these e-scooters might require a battery replacement. To this end, we develop the first stochastic model to capture the battery life dynamics of e-scooters of a large scooter network. In our model, we assume that e-scooter batteries are removable and replaced by agents called swappers. Thus, to gain some insight about the large scale dynamics of the system, we prove a mean field limit theorem and a functional central limit theorem for the fraction of e-scooters that lie in a particular interval of battery life. Exploiting the mean field limit and the functional central limit theorems, we develop an algorithm for determining the number of swappers that are needed to guarantee levels of probabilistic performance of the system. Finally, we show through a stochastic simulation and real data that our stochastic model captures the relevant dynamics.

Suggested Citation

Pender, Jamol and Tao, Shuang and Wikum, Anders, A Stochastic Model for Electric Scooter Systems (April 21, 2020). Available at SSRN: https://ssrn.com/abstract=3582320 or http://dx.doi.org/10.2139/ssrn.3582320

Jamol Pender

Cornell University - School of Operations Research and Industrial Engineering ( email )

Ithaca, NY
United States

Shuang Tao (Contact Author)

Cornell University - Operations Research & Industrial Engineering ( email )

237 Rhodes Hall
Ithaca, NY 14853
United States

Anders Wikum

affiliation not provided to SSRN

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