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

Abolfazl Karimpour, Aryan Hosseinzadeh and Robert Kluger

Journal of Transport Geography, 2023, vol. 109, issue C

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.

Keywords: Dockless electric scooters; E-scooter service area; OD trip data; Agglomerative hierarchical clustering algorithm; Convex hull algorithm (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:109:y:2023:i:c:s0966692323000510

DOI: 10.1016/j.jtrangeo.2023.103579

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