Abstract
This research project will investigate road user interactions with e-scooters. The primary objective will assess e-scooter rider interactions with other road user by data mining the VT E-Scooter Deployment project fixed camera video database that is currently being collected on Virginia Tech campus in Blacksburg. The VT E-Scooter Deployment project is a joint venture with Spin and VTTI to assess safety, convenience, and nuisance factors that may occur with the deployment of e-scooters on a college campus. Using three or four strategically located fixed cameras located proximal to campus bus stops, the data will be reviewed to 1) determine e-scooter presence 2) capture e-scooter interactions with other road users, 3) classify these interactions for severity and 4) record general behavior of e-scooter riders (e.g. helmet use, backpack/carriage of other items, speed, following general rules of road). Analyses will provide greater understanding of e-scooter rider interactions with other road users and potential countermeasures to improve safety for all road users.
Project Highlights
- The analysis showed that e-scooters pose the most threat to pedestrians due to their higher speed and the greater vulnerability of pedestrians.
- The results also showed that the e-scooter riders adjusted their operation rules based on the traffic environment.
- These results suggest it might be safer to operate e-scooters in designated lanes, bike lanes, or on roadways with a speed limit of 25 mph or less. Additional countermeasures to separate e-scooter traffic from vehicles may be required on roadways with faster speed limits.
Final Report
EWD & T2 Products
Served as one of the topics in class, Human Factors in Transportation; course slides can be found here.
Presented the results of the study at the 2022 SDITE Student Leadership Summit.
Presented the results at the 2021 TRB annual conference; the poster can be found here.
Student Impact Statement -Yubin Hong (pdf): The student(s) working on this project provided an impact statement describing what the project allowed them to learn/do/practice and how it benefited their education.
The student experienced how to successfully present the study’s progress and results to the stakeholders.
Presentations/Publications
Currently, a journal article is being prepared and will be submitted to fitting journals by 08/31/2022. Safe-D will update the project site when the article is accepted.
Final Dataset
The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/EISMEV.
Research Investigators (PI*)
Charlie Klauer (VTTI/VT)*
Yubin Hong (VT-Student)
Jean Paul Talledo Vilela (VTTI/VT)
Kevin Grove (VTTI/VT)
Project Information
Start Date: 2020-01-01
End Date: 2022-07-05
Status: Complete
Grant Number: 69A3551747115
Total Funding: $100,000
Source Organization: Safe-D National UTC
Project Number: VTTI-00-030
Safe-D Theme Areas
Transportation as a Service
Automated Vehicles
Big Data Analytics
Safe-D Application Areas
Vulnerable Users
Risk Assessment
Performance Measures
More Information
Sponsor Organization
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States
Performing Organization
Virginia Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061
USA