Exploring spatial heterogeneity of e-scooter’s relationship with ridesourcing using explainable machine learning

The expansion of e-scooter sharing system has introduced several novel interactions within the existing transportation system. However, few studies have explored how spatial contexts influence these interactions. To fill this gap, this study explored the spatial heterogeneity in e-scooter’s relationship with ridesourcing using data from Chicago, IL. The authors developed a Light Gradient Boosting Machine (LightGBM) to estimate e-scooter sharing usage using ridesourcing trips along with associated built environment and socio-demographic variables. The model was interpreted using SHapley Additive exPlanations (SHAP). Results indicated that the threshold effects, where the positive relationship between e-scooter sharing and ridesourcing significantly weakened beyond a certain value, were more pronounced in areas with lower population density, fewer jobs, and fewer young, highly educated population. This is primarily attributed to the limited competitiveness of e-scooter sharing in these areas. These findings can assist cities in harmonizing e-scooter sharing and ridesourcing thus promoting sustainable transportation systems.

• Record URL:
  • https://doi.org/10.1016/j.trd.2024.104452
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1361920924004097
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Jiao, Junfeng
  • Xu, Yiming
  • 0000-0002-2983-1751
  • Li, Yang
  • 0000-0003-3018-1490
• Publication Date: 2024-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 104452
• Serial:
  • Transportation Research Part D: Transport and Environment
  • Volume: 136
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1361-9209
  • Serial URL: http://www.sciencedirect.com/science/journal/13619209

Subject/Index Terms

• TRT Terms: Electric vehicles; Heterogeneity; Machine learning; Ridesourcing; Scooters; Spatial analysis
• Geographic Terms: Chicago (Illinois)
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01933554
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 14 2024 3:57PM