AI-Driven Feedback Systems Reduce Sidewalk Riding on Shared E-Scooters
This study examines the impact of AI-based feedback and speed restrictions on reducing sidewalk riding in shared e-scooters. In partnership with Spin, 100 scooters in Santa Monica, California were fitted with computer vision, with feedback features activated on half. Data from 488 trips revealed that feedback-equipped scooters spent 22% less time and 20% less distance on sidewalks. Nearly half of riders used sidewalks for less than 10% of their trip, while around 10% spent over 60% of trip time on sidewalks, regardless of feedback. These results suggest AI feedback modifies behavior but doesn’t fundamentally change diverse riding patterns.
Authors
- Daniel Malarkey (ORCID: https://orcid.org/0000-0002-8422-7071)
- Don MacKenzie (ORCID: https://orcid.org/0000-0002-0344-2344)
- Mohammad Mehdi Oshanreh (ORCID: https://orcid.org/0009-0008-4589-4403)
Institutions
- University of Washington (US)
Publication Details
- Journal
- Findings
- Published
- 2024-11-19
- DOI
- https://doi.org/10.32866/001c.125892
- Citations
- 1
- Primary Topic
- Evacuation and Crowd Dynamics
- Type
- article
- Field-Weighted Citation Impact
- 0.34
Funders
- U.S. Department of Transportation