Evaluating the Effectiveness of Computer Vision Systems Mounted on Shared Electric Kick Scooters to Reduce Sidewalk Riding

MacKenzie, Don; Malarkey, Daniel; Oshanreh, Mohammad Mehdi; Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART); University of Washington

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Evaluating the Effectiveness of Computer Vision Systems Mounted on Shared Electric Kick Scooters to Reduce Sidewalk Riding

2023-04-01
By MacKenzie, Don ; Malarkey, Daniel ; Oshanreh, Mohammad Mehdi

[PDF-1.62 MB]

English

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Creators:

MacKenzie, Don ; Malarkey, Daniel ; Oshanreh, Mohammad Mehdi

MacKenzie, Don ; Malarkey, Daniel ; Oshanreh, Mohammad Mehdi Less -
Corporate Creators:

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART) ; University of Washington

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART) ; University of Washington Less -
Corporate Contributors:

United States. Department of Transportation. Federal Highway Administration ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology

United States. Department of Transportation. Federal Highway Administration ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Subject/TRT Terms:

[+]

Benefit Cost Analysis Computer Vision Evaluation Pilot Studies Scooters Shared Mobility Sidewalks Vehicle Sharing
Resource Type:

Tech Report
Geographical Coverage:

California ; United States

California ; United States Less -
Edition:

Final Report, 6/1/2022 - 2/28/2023
Corporate Publisher:

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)
Abstract:

The objective of this study was to assess the impact of feedback and speed limitations on the riding behavior of e-scooter riders on sidewalks. To do this, we used data provided by Spin, a US-based micromobility company, on Santa Monica e-scooters that were equipped with an AI camera to monitor surface type. We conducted an experiment in which 50 e-scooters had their feedback mechanisms turned off, while the rest 50 had them on. The study was conducted from November 23, 2022 to February 14, 2023, during which time 488 trips were made within the city of Santa Monica, California. We analyzed the data by calculating the time and distance between consecutive events within a trip, and assuming the distance between two GPS coordinates in the events was the actual path taken by the rider. Empirical cumulative distribution function (ECDF) plots and Kolmogorov-Smirnov tests indicated a statistically significant reduction in the fractions of trip time and distance that were spent on sidewalks, and in the length and duration of individual segments of sidewalk riding. To assess whether the feedback decreased the likelihood of choosing the sidewalk as the next surface when the rider is on the street or bike lane, we used a binary logistic regression model. The models' results revealed a statistically significant reduction in transitions onto sidewalks when riders were on feedback-enabled scooters. This suggests that feedback mechanisms can be valuable tools in guiding e-scooter riders' decisions on where to ride, potentially reducing conflicts between pedestrians and scooter riders.
Format:

PDF
Funding:

69A3551747119
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:0f98152c5d0bb1b406c2a2eb6799eda058169c9c271ce76e219a57ff3bce546b
Download URL:

https://rosap.ntl.bts.gov/view/dot/67553/dot_67553_DS1.pdf
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Evaluating the Effectiveness of Computer Vision Systems Mounted on Shared Electric Kick Scooters to Reduce Sidewalk Riding

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