The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
권호 표지
DOI QR코드

DOI QR Code

Study on Shared E-scooter Usage Characteristics and Influencing Factors

공유 전동킥보드 이용 특성 및 영향요인에 관한 연구

  • Received : 2020.12.28
  • Accepted : 2021.01.28
  • Published : 2021.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, shared dockless e-scooter usage has rapidly increased, rather than the station-based shared mobility service, because of convenience. This transition leads to new social problems in urban areas such as increased traffic accidents and hindrance of pedestrian environments. In this study, we analyze the usage characteristics of shared e-scooters in Seoul, and identify factors influencing demand for shared e-scooters by developing a negative binomial regression model. As a result, the usage characteristics show that the average trip distance, the average trip duration, and the average trip speed were 1.5km, 9.4min, and 10.3km/h, respectively. Demographic factor, transport facility factors, land use factors, and weather factors have statistically significant impacts on demand for shared e-scooters. The results of this study will be used as basic data for suggesting effective operation strategies for areas with higher shared e-scooter demand and for establishing transport policies for facilitating shared e-scooter usage.

공유 전동킥보드는 기존 'station 기반의 공유 교통서비스에 비해 편리한 dockless 방식으로 제공되어 이용이 급격하게 증가하고 있다. 이에 따라 교통사고, 보행환경 저해 등 새로운 도시문제가 발생하고 있다. 본 연구에서는 서울시를 대상으로 최근 급격하게 성장하고 있는 공유전동킥보드의 이용 특성을 분석하고, 음이항 회귀모형을 통해 서울시 집계구 단위의 공유 전동킥보드 이용수요에 영향을 미치는 요인에 대하여 규명하였다. 이용특성 분석결과 평균 이용거리는 1.5km, 이용시간은 9.4분, 이동속도는 10.3km/h로 나타났다. 공유 전동킥보드의 이용수요에 대한 영향요인을 살펴보면, 인구지표, 교통시설지표, 토지이용지표, 기상지표가 통계적으로 유의하게 영향을 미치는 것으로 분석되었다. 본 연구의 결과는 향후 공유 전동킥보드의 이용수요가 많은 지역에 대한 효율적인 운영 및 이용 활성화를 위한 교통정책 수립의 기초자료로 활용될 수 있을 것으로 기대된다.

Keywords

References

  1. Almannaa M. H., Ashqar H. I., Elhenawy M., Masoud M., Rakotonirainy A. and Rakha H.(2020), "A comparative analysis of e-scooter and e-bike usage patterns: Findings from the City of Austin, TX," International Journal of Sustainable Transportation, pp.1-9.
  2. Bai S. and Jiao J.(2020), "Dockless E-scooter usage patterns and urban built environments: A comparison study of Austin, TX, and Minneapolis, MN," Travel Behaviour and Society, vol. 20, pp.264-272. https://doi.org/10.1016/j.tbs.2020.04.005
  3. Campbell A. A., Cherry C. R., Ryerson M. S. and Yang X.(2016), "Factors influencing the choice of shared bicycles and shared electric bikes in Beijing," Transportation Research Part C: Emerging Technologies, vol. 67, pp.399-414. https://doi.org/10.1016/j.trc.2016.03.004
  4. Caspi O., Smart M. J. and Noland R. B.(2020), "Spatial associations of dockless shared e-scooter usage," Transportation Research Part D: Transport and Environment, vol. 86, 102396.
  5. Chen Y. W., Cheng C. Y., Li S. F. and Yu C. H.(2018), "Location optimization for multiple types of charging stations for electric scooters," Applied Soft Computing, vol. 67, pp.519-528. https://doi.org/10.1016/j.asoc.2018.02.038
  6. Choi M. H. and Jung H. Y.(2020), "A Study on the Influencing Factor of Intention to Use Personal Mobility Sharing Services," J. Korean Soc. Transp., vol. 38, no.1, pp.1-13. https://doi.org/10.7470/jkst.2020.38.1.001
  7. Clewlow R. R.(2019), "The Micro-Mobility Revolution: The Introduction and Adoption of Electric Scooters in the United States," 98th Annual Meeting of the Transportation Research Board.
  8. Espinoza W., Howard M., Lane J. and Van Hentenryck P.(2019), Shared E-scooters: Business, Pleasure, or Transit?, arXiv 2019 arXiv:1910.05807.
  9. Fang K., Agrawal A. W., Steele J., Hunter J. J. and Hooper A. M.(2018), Where Do Riders Park Dockless, Shared Electric Scooters? Findings from San Jose, California, Mineta Transportation Institute.
  10. James O., Swiderski J. I., Hicks J., Teoman D. and Beuhler R.(2019), "Pedestrians and e-scooters: An initial look at e-scooter parking and perceptions by riders and non-riders," Sustainability(Switzerland), vol. 11, no. 20, 5591.
  11. Lee M., Chow J. Y., Yoon G. and He B. Y.(2019), Forecasting e-scooter competition with direct and access trips by mode and distance in New York City, arXiv 2019 arXiv:1908.08127v1.
  12. Liu M., Seeder S. and Li H.(2019), "Analysis of E-Scooter Trips and Their Temporal Usage Patterns, Institute of Transportation Engineers," ITE Journal, vol. 89, no. 6, pp.44-49.
  13. McKenzie G.(2019), "Spatiotemporal comparative analysis of scooter-share and bike-share usage patterns in Washington, DC.," Journal of Transport Geography, vol. 78, pp.19-28. https://doi.org/10.1016/j.jtrangeo.2019.05.007
  14. Shaheen S., Bell C., Cohen A. and Yelchuru B.(2017), "Travel behavior: Shared mobility and transportation equity," Report PL-18-007, Federal Highway Administration (FHWA).
  15. Sikka N., Vila C., Stratton M., Ghassemi M. and Pourmand A.(2019), "Sharing the sidewalk: A case of E-scooter related pedestrian injury," The American Journal of Emergency Medicine, vol. 37, no. 9, pp.1807.e5-1807.e7. https://doi.org/10.1016/j.ajem.2019.06.017
  16. Trivedi T. K., Liu C., Antonio A. L. M., Wheaton N., Kreger V., Yap A., Schrigher D. and Elmore J. G.(2019), "Injuries associated with standing electric scooter use," JAMA Network Open, vol. 2, no. 1, pp.e187381-e187381. https://doi.org/10.1001/jamanetworkopen.2018.7381
  17. Zou Z., Younes H., Erdogan S. and Wu J.(2020), "Exploratory Analysis of Real-Time E-Scooter Trip Data in Washington, DC.," Transportation Research Record, vol. 2674, no. 8, pp.285-299. https://doi.org/10.1177/0361198120919760