E-scooters in urban infrastructure: Understanding sidewalk, bike lane, and roadway usage from trajectory data

• Published in: Case studies on transport policy 2021-09, Vol.9 (3), p.983-994
• Main Authors: Zuniga-Garcia, Natalia, Ruiz Juri, Natalia, Perrine, Kenneth A., Machemehl, Randy B.
• Format: Article
• Language: English
• Subjects: Data privacy; Micromobility; Public policy; Shared electric dockless scooters
• ISSN: 2213-624X; 2213-6258
• DOI: 10.1016/j.cstp.2021.04.004

•We assess the integration of e-scooters into the urban infrastructure.•An average e-scooter trip distance is split between sidewalks, bike lanes and roadways.•Approximately 60 percent of the roadway trips are made on principal arterials.•The mean speed of trips made on sidewalks is lower than on other infrastructure.•Trajectory data can provide insightful analysis to understand and regulate e-scooters. This research considers the problem of using trajectory data in the Mobility Data Specification (MDS) standard to conduct meaningful analyses of infrastructure use by e-scooters without compromising personally identifiable information (PII). We assess the integration of e-scooters into the urban infrastructure in Austin, Texas, using trip trajectory data from an e-scooter provider company and infrastructure geographic inventory information. Our analysis uses more than eleven million location points from approximately 80,000 e-scooter trips made over a year, which accounts for 1.4 percent of the total e-scooter trips made in the city during the same period. Our results suggest that an average e-scooter trip distance is split between sidewalks (18 percent), bike lanes (11 percent), and roadways (33 percent), with 38 percent across other unclassified areas. Furthermore, approximately 60 percent of the roadway trips are made on principal arterials, and bike lane users prefer paths with medium to high level of comfort. An analysis of variance suggests that the mean speed of trips made on sidewalks is slightly lower (6 to 8 percent) than on other types of infrastructure, and weekday and AM peak hours present higher speeds. This study illustrates the potential use of trajectory data to provide insightful analysis to help understand and regulate the use of emerging mobility services on the current urban infrastructure. It also highlights the importance of providing and maintaining geographic urban inventory data. Even though our analyses were conducted using raw data points, we also discuss how partially aggregated data without PII could be used to provide similar insights, which can inform the development and extension of data sharing policies.