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

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

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.

Suggested Citation

  • Shah, Nitesh R. & Guo, Jing & Han, Lee D. & Cherry, Christopher R., 2023. "Why do people take e-scooter trips? Insights on temporal and spatial usage patterns of detailed trip data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:transa:v:173:y:2023:i:c:s0965856423001258
    DOI: 10.1016/j.tra.2023.103705
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    1. Shah, Nitesh R. & Ziedan, Abubakr & Brakewood, Candace & Cherry, Christopher R., 2023. "Shared e-scooter service providers with large fleet size have a competitive advantage: Findings from e-scooter demand and supply analysis of Nashville, Tennessee," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    2. Yin, Zehui & Rybarczyk, Greg & Zheng, Anran & Su, Lin & Sun, Bingrong & Yan, Xiang, 2024. "Shared micromobility as a first- and last-mile transit solution? Spatiotemporal insights from a novel dataset," Journal of Transport Geography, Elsevier, vol. 114(C).
    3. Yang, Ying & Zhang, Wei & Lin, Hongyi & Liu, Yang & Qu, Xiaobo, 2024. "Applying masked language model for transport mode choice behavior prediction," Transportation Research Part A: Policy and Practice, Elsevier, vol. 184(C).

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