Why do people take e-scooter trips? Insights on temporal and spatial usage patterns of detailed trip data

Nitesh R. Shah, Jing Guo, Lee D. Han and Christopher R. Cherry

Transportation Research Part A: Policy and Practice, 2023, vol. 173, issue C

Abstract: Electric scooters (e-scooters) are becoming one of the most popular micromobility options in the United States. Although there is some evidence of increased mobility, reduced carbon emissions, replaced car trips, and associated public health benefits, there is little known about the patterns of e-scooter use. This study proposes a framework for high-resolution analysis of micromobility data based on temporal, spatial, and weather attributes. As a case study, we scrutinized more than one million e-scooter trips of Nashville, Tennessee, from September 1, 2018, to August 31, 2019. Weather data and land use data from the Nashville Travel Demand Model and scraping of Google Maps Point of Interest (POI) data complemented the trip data. The combination of Principal Component Analysis (PCA) and a K-means unsupervised machine learning algorithm identified five distinct e-scooter usage patterns, namely morning work/school, daytime short errand, social, nighttime entertainment district, and utilitarian trips.

Keywords: E-scooters; Micromobility; Spatiotemporal analysis; Big data; Unsupervised machine learning (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.tra.2023.103705

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