Graduation Year

2022

Document Type

Thesis

Degree

M.S.C.E.

Degree Name

MS in Civil Engineering (M.S.C.E.)

Degree Granting Department

Civil and Environmental Engineering

Major Professor

Yu Zhang, Ph.D.

Committee Member

Fred L. Mannering, Ph.D.

Committee Member

Peng Chen, Ph.D.

Committee Member

Qing Lu, Ph.D.

Keywords

E-scooter Sharing Program, Econometric Modeling, Injury Level of Shared E-scooter Accident, Possibility of Shared E-scooter Accident

Abstract

  • Objective: To identify significant factors that affect the possibility of shared e-scooter related crashes in Tampa and St. Petersburg, FL, and also to capture determinants contributing to the level of injury of shared e-scooter related crashes in Tampa, FL.
  • Background: Over the past few years, shared e-scooters have become a common form of micromobility; however, safety concerns have been raised due to the increased number of injury accidents involving e-scooters.
  • Data and data sources: Survey questionnaires were designed to collect data from the general public on e-scooter sharing programs in Tampa and St. Petersburg, FL. The collected data include respondents’ demographics, travel behaviors, crash experiences if they have been involved, and mode shifting.
  • Methods: Random parameters binary logit and random parameters ordered probit models were developed for modeling crash possibility and severity level of shared e-scooter injuries.
  • Results: For the random parameters logit model to understand the likelihood of shared e- scooter accidents, significant variables for Tampa, FL include users who were classified as young (old), identified themselves as white, have a college degree or higher- level education, have more than $100,000 annual household income, do not ride shared e- scooters regularly and ride shared e-scooters on sidewalks or bikelanes. Significant variables for St. Petersburg, FL include users who were classified as old (>40 years old), have a college degree or higher-level education. And regarding respondents’ travel behaviors, users who do not ride shared e-scooter regularly and ride them on bikelanes, and do not wear a helmet were also found significant. The outcomes of the random parameters ordered probit model for the city of Tampa show that significant variables that affect the level of shared e-scooter crash severity include users having a college degree or higher-level education. In addition, experiencing falling from the e-scooter type of accidents, riding at nights, and riding shared e-scooters on sidewalks or bikelanes was found to be significant for affecting the severity level shared e-scooter crashes.

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