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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Vydáno v:Journal of transport geography Ročník 109; s. 103579
Hlavní autoři: Karimpour, Abolfazl, Hosseinzadeh, Aryan, Kluger, Robert
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.05.2023
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ISSN:0966-6923, 1873-1236
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Abstract 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.
AbstractList 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.
ArticleNumber 103579
Author Hosseinzadeh, Aryan
Karimpour, Abolfazl
Kluger, Robert
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  surname: Hosseinzadeh
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  givenname: Robert
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  email: robert.kluger@louisville.edu
  organization: Department of Civil and Environmental Engineering, University of Louisville, W.S. Speed, Room 112, Louisville, KY 40292, United States of America
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Keywords Dockless electric scooters
OD trip data
E-scooter service area
Agglomerative hierarchical clustering algorithm
Convex hull algorithm
Language English
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Snippet 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...
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SubjectTerms Agglomerative hierarchical clustering algorithm
Convex hull algorithm
Dockless electric scooters
E-scooter service area
OD trip data
Title A data-driven approach to estimating dockless electric scooter service areas
URI https://dx.doi.org/10.1016/j.jtrangeo.2023.103579
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