Keywords

Econometric analysis; shared mobility; e-scooters; micromobility

Abstract

This dissertation conducted an extensive examination of dockless e-scooter dynamics using high-resolution trip data from Austin, Texas. Four studies were conducted to capture the multifaceted nature of e-scooter operations and demand. The first study aimed to identify and quantify the influence of contributing factors affecting e-scooter demand by partitioning the data by time period for weekdays and weekends. Utilizing a joint panel linear regression (JPLR) model, significant associations were observed between e-scooter demand and variables such as sociodemographic attributes, transportation infrastructure, land use, meteorological attributes, and situational factors. The second study shifted focus to shared e-scooter origin-destination (OD) flows in the urban region. By employing a joint binary logit-fractional split model, e-scooter OD flows were analyzed, emphasizing variations across distinct time periods and the subsequent implications for e-scooter deployment and rebalancing strategies. The third study delved into e-scooter utilization efficiency, introducing a time-to-book (TtB) measure. Through a Mixed Grouped Ordered Logit (MGOL) model, the study highlighted variations between regular and peak weeks, offering operators a chance to enhance fleet utilization. The final study addressed the broader context of the e-scooter industry, investigating the impact of the COVID-19 pandemic. By analyzing datasets spanning January 2019 through December 2021, a spatial approach illuminated changes in e-scooter demand patterns before, during, and after the pandemic, highlighting the effects of COVID-19-related factors and vaccine attributes on e-scooter trends. These collective insights from the four studies provide valuable contributions to understanding and enhancing e-scooter operations in urban landscapes

Completion Date

2023

Semester

Fall

Committee Chair

Eluru, Naveen

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Format

application/pdf

Identifier

DP0028005

URL

https://purls.library.ucf.edu/go/DP0028005

Language

English

Release Date

December 2024

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Campus-only Access)

Campus Location

Orlando (Main) Campus

Download

Restricted to the UCF community until December 2024; it will then be open access.

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