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Spatial analysis of shared e-scooter trips

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  • Hosseinzadeh, Aryan
  • Algomaiah, Majeed
  • Kluger, Robert
  • Li, Zhixia

Abstract

Shared e-scooters have become a common mode of transportation in many cities around the world. E-sooters provide convenient and quick rides for short distances and can act as a connection for first/last mile trips. To date, limited studies have explored the spatial variation of e-scooter trips and there is knowledge to be gained by investigating variables associated with e-scooter trip generation. This study implemented a spatial analysis approach, Geographical Weighted Regression (GWR), to explore how factors relating to demographics, density, diversity, design, urbanism scores, distance to transit and other transportation-related variables influence e-scooter trips in Louisville, KY. More than 400,000 e-scooter trips across 159 Traffic Analysis Zones (TAZs) were included in the study. Results show TAZ-level factors including land use, age distribution, gender distribution, Walk Score and Park Score impacted the density of e-scooters trips in the TAZ. The GWR model showed improvements over a global Ordinary Least Squares (OLS) model. Local goodness of fit ranged from 0.732–0.895 across the study area. This study can help governments and e-scooter sharing companies develop policies that maximize e-scooter use, equity, and accessibility while improving the mobility of cities.

Suggested Citation

  • Hosseinzadeh, Aryan & Algomaiah, Majeed & Kluger, Robert & Li, Zhixia, 2021. "Spatial analysis of shared e-scooter trips," Journal of Transport Geography, Elsevier, vol. 92(C).
    • Handle: RePEc:eee:jotrge:v:92:y:2021:i:c:s0966692321000697
    DOI: 10.1016/j.jtrangeo.2021.103016
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    7. Jin, Scarlett T. & Wang, Lei & Sui, Daniel, 2023. "How the built environment affects E-scooter sharing link flows: A machine learning approach," Journal of Transport Geography, Elsevier, vol. 112(C).
    8. Nigro, Marialisa & Castiglione, Marisdea & Maria Colasanti, Fabio & De Vincentis, Rosita & Valenti, Gaetano & Liberto, Carlo & Comi, Antonio, 2022. "Exploiting floating car data to derive the shifting potential to electric micromobility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 78-93.
    9. 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).
    10. Jin, Scarlett T. & Sui, Daniel Z., 2024. "A comparative analysis of the spatial determinants of e-bike and e-scooter sharing link flows," Journal of Transport Geography, Elsevier, vol. 119(C).
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    12. Zhu, Rui & Kondor, Dániel & Cheng, Cheng & Zhang, Xiaohu & Santi, Paolo & Wong, Man Sing & Ratti, Carlo, 2022. "Solar photovoltaic generation for charging shared electric scooters," Applied Energy, Elsevier, vol. 313(C).
    13. Samadzad, Mahdi & Nosratzadeh, Hossein & Karami, Hossein & Karami, Ali, 2023. "What are the factors affecting the adoption and use of electric scooter sharing systems from the end user's perspective?," Transport Policy, Elsevier, vol. 136(C), pages 70-82.
    14. Coenegrachts, Elnert & Vanelslander, Thierry & Verhetsel, Ann & Beckers, Joris, 2024. "Analyzing shared mobility markets in Europe: A comparative analysis of shared mobility schemes across 311 European cities," Journal of Transport Geography, Elsevier, vol. 118(C).
    15. Maximilian Heumann & Tobias Kraschewski & Tim Brauner & Lukas Tilch & Michael H. Breitner, 2021. "A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage," Sustainability, MDPI, vol. 13(22), pages 1-24, November.
    16. Elena Carrara & Rebecca Ciavarella & Stefania Boglietti & Martina Carra & Giulio Maternini & Benedetto Barabino, 2021. "Identifying and Selecting Key Sustainable Parameters for the Monitoring of e-Powered Micro Personal Mobility Vehicles. Evidence from Italy," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    17. Priyanka Verma & Grant McKenzie, 2024. "Regional comparison of socio-demographic variation in urban E-scooter usage," Environment and Planning B, , vol. 51(7), pages 1548-1562, September.
    18. Mühlematter, Dominik J. & Wiedemann, Nina & Xin, Yanan & Raubal, Martin, 2024. "Spatially-aware station based car-sharing demand prediction," Journal of Transport Geography, Elsevier, vol. 114(C).
    19. Hosseinzadeh, Aryan & Baghbani, Asiye, 2020. "Walking Trip Generation and Built Environment: A Comparative Study on Trip Purposes," MPRA Paper 109025, University Library of Munich, Germany.
    20. Yang, Hongtai & Zheng, Rong & Li, Xuan & Huo, Jinghai & Yang, Linchuan & Zhu, Tong, 2022. "Nonlinear and threshold effects of the built environment on e-scooter sharing ridership," Journal of Transport Geography, Elsevier, vol. 104(C).
    21. Samira Dibaj & Aryan Hosseinzadeh & Miloš N. Mladenović & Robert Kluger, 2021. "Where Have Shared E-Scooters Taken Us So Far? A Review of Mobility Patterns, Usage Frequency, and Personas," Sustainability, MDPI, vol. 13(21), pages 1-27, October.
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