Identifying Significant Factors Affecting the Likelihood and Severity Level of Shared E-scooter Crashes
MS in Civil Engineering (M.S.C.E.)
Degree Granting Department
Civil and Environmental Engineering
Yu Zhang, Ph.D.
Fred L. Mannering, Ph.D.
Peng Chen, Ph.D.
Qing Lu, Ph.D.
E-scooter Sharing Program, Econometric Modeling, Injury Level of Shared E-scooter Accident, Possibility of Shared E-scooter Accident
- 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.
Scholar Commons Citation
Cakici, Recep Can, "Identifying Significant Factors Affecting the Likelihood and Severity Level of Shared E-scooter Crashes" (2022). USF Tampa Graduate Theses and Dissertations.