All Issue

2025 Vol.43, Issue 3 Preview Page

Article

Analysis of Factors Influencing the Route Choice of E-Scooter Users: Focusing on the Discrepancy between Actual Usage Paths and Shortest Paths

전동킥보드 경로선택에 영향을 미치는 주행환경 분석: 실제 이동경로와 최단경로의 차이를 중심으로

Hyewon KIM1
,
Sugie LEE2*
김 혜원1
,
이 수기2*
1M.S., Department of Urban Planning and Engineering, Hanyang University, Seoul 04763, Korea
2Professor, Department of Urban Planning and Engineering, Hanyang University, Seoul 04763, Korea
1한양대학교 도시공학과 석사
2한양대학교 도시공학과 교수
*Corresponding Author
30 June 2025. pp. 311-331
PDF XML Citation
Abstract

The COVID-19 pandemic has accelerated the adoption of personal mobility (PM) devices, with shared e-scooters emerging as a socially distanced mode of travel. In South Korea, where bicycle lanes are often shared with pedestrians, e-scooter users frequently encounter mixed- traffic environments, raising concerns over user safety and infrastructure adequacy. This study examines the factors influencing e-scooter route choice in Seoul using GPS trajectory data collected from a major shared e-scooter operator. To quantify route efficiency, the Shared Route Ratio—defined as the proportion of the actual route overlapping with the shortest path—is introduced as the dependent variable. The results of multiple regression analysis indicate that nighttime trips and routes with sufficient lighting and signalized intersections are positively associated with higher shared route ratios, suggesting a preference for direct and safer travel paths. Conversely, price discounts, membership status, and proximity to riverside environments are associated with lower shared route ratios, reflecting a tendency toward more diverse or leisure-oriented routes. These findings highlight the contextual characteristics of e-scooter use within South Korea’s PM infrastructure and underscore the importance of expanding spatial separation from pedestrians, increasing dedicated lane coverage, and improving intersection- level traffic management to enhance safety and operational efficiency.

Keywords
built environment
e-scooter
GPS trajectory
route choice
route deviation

COVID-19 확산과 사회적 거리 두기 정책으로 인해 개인형 이동수단(Personal Mobility, PM), 특히 공유 전동킥보드의 이용이 급증하였다. 그러나 국내 자전거도로의 대부분은 자전거·보행자 혼용도로 중심으로 구성되어 있어, 전동킥보드 이용자는 보도와 차도를 모두 주행하게 되며 이로 인해 보행자, 차량 운전자, 전동킥보드 이용자 모두에게 안전상의 문제를 초래하고 있다. 이에 본 연구는 국내 PM 주행환경이 전동킥보드 이용자의 경로선택에 미치는 영향을 실증적으로 분석하고자 하였다. 서울시 내 공유 전동킥보드의 실제 주행 궤적 데이터를 활용하여, 최단경로와 실제 이동경로 간의 공유 경로 비율(Shared Route Ratio)을 종속변수로 설정하고 개인·통행 특성, 물리적·인지적 환경 특성에 따른 다중회귀분석을 수행하였다. 분석 결과, 심야 시간대에 주행할수록 공유 경로 비율이 높게 나타나 최단경로를 따라 주행하는 반면, 할인 혜택을 받거나 정기회원일수록 우회경로를 선택하는 경향이 나타났다. 또한, 신호등과 가로등 밀도가 높은 지역에서는 공유 경로 비율이 증가하였으나, 수변 주행비율이 높은 경로에서는 우회경로를 선택하는 경향이 확인되었다. 이는 전동킥보드 이용자가 이동경로 선택 시 효율성뿐만 아니라 쾌적성과 여가 목적 또한 고려한다는 것을 시사한다. 본 연구는 국내 전동킥보드 주행환경을 반영하여 개인·통행 특성과 다양한 주행환경이 전동킥보드 이용자의 경로선택에 미치는 영향을 정량적으로 규명하였단 점에서 의의가 있다. 이를 통해 전동킥보드 이용자의 안전한 주행환경 조성을 위한 정책적 시사점을 제시하였으며, 향후 PM 인프라 개선 방향과 교차로 혼잡 완화, 보행자와의 공간적 분리 등의 정책 수립에 기여할 것으로 기대된다.

키워드
주행환경
전동킥보드
이동 궤적
경로선택
우회경로
References
1

Ahn D. E., Lee K. H., Ko E. J. (2021), Analysis of Factors Influencing the Satisfaction of Use Environment of Shared Electric Kickboard: The Case of Seoul South Korea, Journal of the Architectural Institute of Korea, 37(7), 3-11.

2

Aldred R., Elliott B., Woodcock J., Goodman A. (2017), Cycling Provision Separated from Motor Traffic: A Systematic Review Exploring Whether Stated Preferences Vary by Gender and Age, Transport Reviews, 37(1), 29-55.

10.1080/01441647.2016.120015628190905PMC5259802
3

Almannaa M. H., Ashqar H. I., Elhenawy M., Masoud M., Rakotonirainy A., Rakha H. (2021), A Comparative Analysis of E-Scooter and E-Bike Usage Patterns: Findings from the City of Austin TX, International Journal of Sustainable Transportation, 15(7), 571-579.

10.1080/15568318.2020.1833117
4

Baek J. H., Yoon T. K., Kim J. H. (2023), A Study on the Improvement of Mobility Policy Using PM Big Data, Korea Research Institute for Human Settlements, Sejong, Korea.

5

Broach J., Dill J., Gliebe J. (2012), Where Do Cyclists Ride? A Route Choice Model Developed With Revealed Preference GPS Data, Transportation Research Part A: Policy and Practice, 46(10), 1730-1740.

10.1016/j.tra.2012.07.005
6

Burlacu F. A., Rogers L. (2019), Speed Variation Analysis: A Case Study for Thailand's Roads, No.144254, 1-10, The World Bank.

7

Caspi O., Smart M. J., Noland R. B. (2020), Spatial Associations of Dockless Shared E-Scooter Usage, Transportation Research Part D: Transport and Environment, 86, 102396.

10.1016/j.trd.2020.10239632834737PMC7329686
8

Chen P., Shen Q., Childress S. (2018), A GPS Data-Based Analysis of Built Environment Influences on Bicyclist Route Preferences, International Journal of Sustainable Transportation, 12(3), 218-231.

10.1080/15568318.2017.1349222
9

Choi J. H. (2019), Micromobility Focused on Shared E-Scooters, KDB Future Strategy Research Institute, Seoul, Korea.

10

Chou K. Y., Paulsen M., Nielsen O. A., Jensen A. F. (2023), Analysis of Cycling Accessibility Using Detour Ratios: A Large-Scale Study Based on Crowdsourced GPS Data, Sustainable Cities and Society, 93, 104500.

10.1016/j.scs.2023.104500
11

Christoforou Z., de Bortoli A., Gioldasis C., Seidowsky R. (2021), Who Is Using E-Scooters and How? Evidence From Paris, Transportation Research Part D: Transport and Environment, 92, 102708.

10.1016/j.trd.2021.102708
12

Chung J., Yao E., Pan L., Ko J. (2024), Understanding the Route Choice Preferences of Private and Dock-Based Public Bike Users Using GPS Data in Seoul, South Korea, Journal of Transport Geography, 116, 103845.

10.1016/j.jtrangeo.2024.103845
13

Ciscal-Terry W., Dell'Amico M., Hadjidimitriou N. S., Iori M. (2016), An Analysis of Drivers' Route Choice Behaviour Using GPS Data and Optimal Alternatives, Journal of Transport Geography, 51, 119-129.

10.1016/j.jtrangeo.2015.12.003
14

Cooper C. H., Chiaradia A. J. (2020), sDNA: 3-D Spatial Network Analysis for GIS, CAD, Command Line & Python, SoftwareX, 12, 100525.

10.1016/j.softx.2020.100525
15

Cubells J., Miralles-Guasch C., Marquet O. (2023), E-Scooter and Bike-Share Route Choice and Detours: Modelling the Influence of Built Environment and Sociodemographic Factors, Journal of Transport Geography, 111, 103664.

10.1016/j.jtrangeo.2023.103664
16

Fitch D. T., Handy S. L. (2020), Road Environments and Bicyclist Route Choice: The Cases of Davis and San Francisco CA, Journal of Transport Geography, 85, 102705.

10.1016/j.jtrangeo.2020.102705
17

Ghanayim M., Bekhor S. (2018), Modelling Bicycle Route Choice Using Data from a GPS-Assisted Household Survey, European Journal of Transport and Infrastructure Research, 18(2), 158-177.

10.18757/EJTIR.2018.18.2.3228
18

Gibson H., Curl A., Thompson L. (2022), Blurred Boundaries: E-Scooter Riders' and Pedestrians' Experiences of Sharing Space, Mobilities, 17(1), 69-84.

10.1080/17450101.2021.1967097
19

Giraud T. (2022), OSRM: Interface between R and the OpenStreetMap-Based Routing Service OSRM, Journal of Open Source Software, 7(78), 4574.

10.21105/joss.04574
20

Guo Y., Zhang Y. (2021), Understanding Factors Influencing Shared E-Scooter Usage and Its Impact on Auto Mode Substitution, Transportation Research Part D: Transport and Environment, 99, 102991.

10.1016/j.trd.2021.102991
21

Han J. W., Ahn S. Y., Kim M. S., Han D. H., Lee S. G. (2022), Analysis of the Priority Supply Areas of Personal Mobility in Seoul Korea: Focusing on the Travel Characteristics of First-and-Last Miles, Journal of Korea Planning Association, 57(1), 42-56.

10.17208/jkpa.2022.02.57.1.42
22

Hong J. Y., So Y. I., Lee K. Y., Kim J. M., Lee S. G. (2022), Analysis of Shared Electric Scooter Usage Behavior and Determining Factors of User Satisfaction: Focused on IPA Method, Journal of Korea Planning Association, 57(2), 92-107.

10.17208/jkpa.2022.04.57.2.92
23

Hsueh C., Lin J. J. (2023), Influential Factors of the Route Choices of Scooter Riders: A GPS-Based Data Study, Journal of Transport Geography, 113, 103719.

10.1016/j.jtrangeo.2023.103719
24

Innoforest (2023), Hot 7 Shared Mobility Startups With Consecutive Revenue Growth Over the Past Three Years, Retrieved From https://www.innoforest.co.kr/report/NS00000131, 2024.05.21.

25

Karpinski E. (2021), Estimating the Effect of Protected Bike Lanes on Bike-Share Ridership in Boston: A Case Study on Commonwealth Avenue, Case Studies on Transport Policy, 9(3), 1313-1323.

10.1016/j.cstp.2021.06.015
26

Kim S. J. (2022), Optimal Rebalancing Strategy for Shared E-Scooter Using Genetic Algorithm, Doctoral Dissertation, Hongik University Graduate School, Seoul, Korea.

10.1155/2023/2696651
27

Korea Transportation Safety Authority (KOTSA) (2021), A Study on the Safety Standards for Personal Mobility Devices, Korea Transportation Safety Authority, Gyeongbuk, Korea.

28

Kweon J. H., Lee S. G. (2024), Analysis of Influencing Factors of Unauthorized Electric Kickboard Parking by Type in Seoul Korea, Journal of Korea Planning Association, 59(2), 104-120.

10.17208/jkpa.2024.04.59.2.104
29

Lin Z., Fan W. D. (2020), Modeling Bicycle Volume Using Crowdsourced Data From Strava Smartphone Application, International Journal of Transportation Science and Technology, 9(4), 334-343.

10.1016/j.ijtst.2020.03.003
30

López-Dóriga I., Vich G., Koch S., Khomenko S., Marquet O., Roig-Costa O., Mueller N. (2022), Health Impacts of Electric Micromobility Transitions in Barcelona: A Scenario Analysis, Environmental Impact Assessment Review, 96, 106836.

10.1016/j.eiar.2022.106836
31

Maas S., Nikolaou P., Attard M., Dimitriou L. (2021), Spatial and Temporal Analysis of Shared Bicycle Use in Limassol Cyprus, Journal of Transport Geography, 93, 103049.

10.1016/j.jtrangeo.2021.103049
32

McKenzie G. (2019), Spatiotemporal Comparative Analysis of Scooter-Share and Bike-Share Usage Patterns in Washington DC, Journal of Transport Geography, 78, 19-28.

10.1016/j.jtrangeo.2019.05.007
33

Mekuria M., Furth P., Nixon H. (2012), Low-Stress Bicycling and Network Connectivity, Mineta Transportation Institute.

34

Meng S. A., Zacharias J. (2021), Street Morphology and Travel by Dockless Shared Bicycles in Beijing, China, International Journal of Sustainable Transportation, 15(10), 788-798.

10.1080/15568318.2020.1813853
35

Misra A., Watkins K. (2018), Modeling Cyclist Route Choice Using Revealed Preference Data: An Age and Gender Perspective, Transportation Research Record, 2672(3), 145-154.

10.1177/0361198118798968
36

Nikiforiadis A., Paschalidis E., Stamatiadis N., Raptopoulou A., Kostareli A., Basbas S. (2021), Analysis of Attitudes and Engagement of Shared E-Scooter Users, Transportation Research Part D: Transport and Environment, 94, 102790.

10.1016/j.trd.2021.102790
37

Oyama Y. (2024), Global Path Preference and Local Response: A Reward Decomposition Approach for Network Path Choice Analysis in the Presence of Visually Perceived Attributes. Transportation Research Part A: Policy and Practice, 181, 103998.

10.1016/j.tra.2024.103998
38

Park Y., Akar G. (2019), Why Do Bicyclists Take Detours? A Multilevel Regression Model using Smartphone GPS Data. Journal of Transport Geography, 74, 191-200.

10.1016/j.jtrangeo.2018.11.013
39

Sa K. E., Seo J. M., Lee S. G. (2020), Analysis of Origin-Destination Characteristics and the Shortest Travel Path of Public Bike Users in the Morning Peak-Hour Period: Focused on the 2017 Travel OD Data of Public Bike Sharing in Seoul, Korea, Journal of the Urban Design Institute of Korea Urban Design, 21(6), 105-120.

10.38195/judik.2020.12.21.6.105
40

Traffic Accident Analysis System (TAAS) (2022), http://taas.koroad.or.kr/sta/acs/gus/selectVhctyTfcacd.do?menuId=WEB_KMP_OVT_MVT_TAG_VTT

41

Yang H., Bao Y., Huo J., Hu S., Yang L., Sun L. (2022), Impact of Road Features on Shared E-Scooter Trip Volume: A Study Based on Multiple Membership Multilevel Model, Travel Behaviour and Society, 28, 204-213.

10.1016/j.tbs.2022.04.005
42

Zhang W., Buehler R., Broaddus A., Sweeney T. (2021), What Type of Infrastructures Do E-Scooter Riders Prefer? A Route Choice Model, Transportation Research Part D: Transport and Environment, 94, 102761.

10.1016/j.trd.2021.102761
43

Zuniga-Garcia N., Juri N. R., Perrine K. A., Machemehl R. B. (2021), E-scooters in Urban Infrastructure: Understanding Sidewalk, Bike Lane, and Roadway Usage from Trajectory Data. Case Studies on Transport Policy, 9(3), 983-994.

10.1016/j.cstp.2021.04.004
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 43
  • No :3
  • Pages :311-331
  • Received Date : 2025-01-15
  • Revised Date : 2025-03-18
  • Accepted Date : 2025-03-18
  • DOI :https://doi.org/10.7470/jkst.2025.43.3.311
Journal Informaiton Journal of Korean Society of Transportation Journal of Korean Society of Transportation
Archives
: 2015 Vol.34~
  • NRF
  • KOFST
  • crossref cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close