A data-driven approach to estimating dockless electric scooter service areas

With the surging usage of e-scooters worldwide, there is a growing interest in understanding different aspects of e-scooters trips and their impact on urban mobility. Further, the emergence of this new mode of transportation has led to questions regarding the spatial accessibility of e-scooters and...

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Bibliographic Details
Published in:Journal of transport geography Vol. 109; p. 103579
Main Authors: Karimpour, Abolfazl, Hosseinzadeh, Aryan, Kluger, Robert
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2023
Subjects:
Agglomerative hierarchical clustering algorithm
Convex hull algorithm
Dockless electric scooters
E-scooter service area
OD trip data
Dockless electric scooters
OD trip data
E-scooter service area
Agglomerative hierarchical clustering algorithm
Convex hull algorithm
ISSN:0966-6923, 1873-1236
Online Access:Get full text
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Summary:With the surging usage of e-scooters worldwide, there is a growing interest in understanding different aspects of e-scooters trips and their impact on urban mobility. Further, the emergence of this new mode of transportation has led to questions regarding the spatial accessibility of e-scooters and understanding how the built environment and urbanism characteristics affect riders' abilities to reach certain destinations. In this study, initially, a data-driven approach was proposed to construct the service areas for dockless e-scooter using origin-destination trip data. Service areas are defined as spatial areas that riders are regularly able to reach via an e-scooter. E-scooter service areas were constructed for traffic analysis zones in Louisville, KY, using agglomerative hierarchical clustering and convex hull algorithms. Then, the relationship between various built environments and urbanism characteristics and the e-scooter service areas was examined using principal component analysis and random forest regression. The results showed that percent of residential properties, length of the block, Walk Score®, Transit Score ®, and Dining and Drinking Score contributed most to the size of the e-scooter service area. The findings of this research offer a transferable method to estimate e-scooter service areas to quantify access to goods and services. Further, the study discusses how the built environment and urbanism characteristics might affect the size of the service areas.
ISSN:0966-6923
1873-1236
DOI:10.1016/j.jtrangeo.2023.103579