Cities with dense networks of shared scooter parking have higher parking compliance
Sian Meng
Zhengzhou University
https://orcid.org/0000-0002-1030-8709
Anne Brown
University of Oregon
https://orcid.org/0000-0001-5009-8331
Calvin Thigpen
Lime
https://orcid.org/0000-0003-4284-2882
Brandon Haydu
Lime
Nicholas J. Klein
Cornell University
https://orcid.org/0000-0002-7596-6147
DOI: https://doi.org/10.5198/jtlu.2025.2469
Keywords: Shared e-scooter, Parking density, Parking compliance, Land use, Parking demand
Abstract
Many cities seek solutions to address public concerns about non-compliant shared electric scooter (e-scooter) parking. One strategy is to provide designated parking areas, called “corrals.” However, it remains unclear how much parking infrastructure is needed to improve compliance, or how this varies by land use. To address this gap, our study investigates: How dense does a network of dedicated shared micromobility parking need to be to increase compliance? How large should parking corrals be to meet demand? And how do these relationships vary by land use? We used e-scooter and built environment data in 12 cities worldwide and conducted descriptive, regression, and non-linear analyses. Results reveal that providing at least 20-30 parking corrals per square kilometer (about 50-80 per square mile or a one-minute walk in gridded areas) dramatically improves parking compliance. The spatial distribution of corrals is particularly important in areas with low corral density, where providing uniform coverage can significantly reduce parking non-compliance rates. Land-use intensity variables are non-linearly associated with parking non-compliance but suggest that parking corrals should have greater capacity in areas with more leisure, mixed-use, office, transit, and tourism destinations compared to places with more commercial or residential destinations. These findings offer direct policy recommendations to improve parking compliance and better match demand.
References
Berg Wincent, B., Jenelius, E., & Burghout, W. (2023). Parkering av elsparkcyklar: Enkätundersökning av effekter och åsikter kring parkeringsförbudet i Stockholm, Göteborg och Malmö (TRITA-ABE-RPT ; 237). Retrieved from https://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1753529&dswid=2335
Breiman, L. (2017). Classification and regression trees. Abingdon-on-Thames, UK: Routledge. https://doi.org/10.1201/9781315139470
Brinkhoff, T. (2023). City population—Population statistics in maps and charts for cities, agglomerations and administrative divisions of all countries of the world. Retrieved from https://www.citypopulation.de/
Brodsky, I. (2018, June 27). H3: Uber’s hexagonal hierarchical spatial index. Uber blog. Retrieved from https://www.uber.com/blog/h3/
Brown, A. (2021). Micromobility, macro goals: Aligning scooter parking policy with broader city objectives. Transportation Research Interdisciplinary Perspectives, 12, 100508. https://doi.org/10.1016/j.trip.2021.100508
Brown, A., Klein, N. J., & Thigpen, C. (2021). Can you park your scooter there? Why scooter riders mis-park and what to do about it. Findings. https://doi.org/10.32866/001c.19537
Brown, A., Klein, N. J., Thigpen, C., & Williams, N. (2020). Impeding access: The frequency and characteristics of improper scooter, bike, and car parking. Transportation Research Interdisciplinary Perspectives, 4, 100099. https://doi.org/10.1016/j.trip.2020.100099
Brown, A., Thigpen, C., & Klein, N. J. (2024). Scooting around the margins: Testing scooter parking design pilots. Findings. https://doi.org/10.32866/001c.127199
Brown, A., Thigpen, C., Klein, N. J., & Ralph, K. (2025). Pilots and shifting public sentiment: Evidence from e-scooters in Eugene (OR). Journal of the American Planning Association, 1–16. https://www.tandfonline.com/doi/abs/10.1080/01944363.2024.2441373
Buehler, R., Broaddus, A., Sweeney, T., Zhang, W., White, E., & Mollenhauer, M. (2021). Changes in travel behavior, attitudes, and preferences among e-scooter riders and nonriders: First look at results from pre and post e-scooter system launch surveys at Virginia Tech. Transportation Research Record, 2675(9), 335–345. https://doi.org/10.1177/03611981211002213
Buehler, R., Broaddus, A., White, E., Sweeney, T., & Evans, C. (2023). An exploration of the decline in e-scooter ridership after the introduction of mandatory e-scooter parking corrals on Virginia Tech’s campus in Blacksburg, VA. Sustainability, 15(1), 226. https://doi.org/10.3390/su15010226
Cervero, R., Sarmiento, O. L., Jacoby, E., Gomez, L. F., & Neiman, A. (2009). Influences of built environments on walking and cycling: Lessons from Bogotá. International Journal of Sustainable Transportation, 3(4), 203–226. https://doi.org/10.1080/15568310802178314
Chen, Z., van Lierop, D., & Ettema, D. (2020). Dockless bike-sharing systems: What are the implications? Transport Reviews, 40(3), 333–353. https://doi.org/10.1080/01441647.2019.1710306
Dill, J., & McNeil, N. (2021). Are shared vehicles shared by all? A review of equity and vehicle sharing. Journal of Planning Literature, 36(1), 5–30. https://doi.org/10.1177/0885412220966732
El-Geneidy, A., Grimsrud, M., Wasfi, R., Tétreault, P., & Surprenant-Legault, J. (2014). New evidence on walking distances to transit stops: Identifying redundancies and gaps using variable service areas. Transportation, 41(1), 193–210.
Ewing, R., & Cervero, R. (2010). Travel and the built environment. Journal of the American Planning Association, 76(3), 265–294. https://doi.org/10.1080/01944361003766766
Fang, K., Agrawal, A., Steele, J., Hunter, J., & Hooper, A. (2018). Where do riders park dockless, shared electric scooters? Findings from San Jose, California. San Jose, CA: Mineta Transportation Institute. https://scholarworks.sjsu.edu/mti_publications/251
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
Furth, P. G., & Rahbee, A. B. (2000). Optimal bus stop spacing through dynamic programming and geographic modeling. Transportation Research Record, 1731(1), 15–22. https://doi.org/10.3141/1731-03
Gössling, S. (2020). Integrating e-scooters in urban transportation: Problems, policies, and the prospect of system change. Transportation Research Part D: Transport and Environment, 79, 102230. https://doi.org/10.1016/j.trd.2020.102230
GPS.gov. (2022). GPS.gov: GPS accuracy. Retrieved from https://www.gps.gov/systems/gps/performance/accuracy/
Hemphill, R., MacArthur, J., Longenecker, P., Desai, G., Nie, L., Ibarra, A., & Dill, J. (2022). Congested sidewalks: The effects of the built environment on e-scooter parking compliance. Journal of Transport and Land Use, 15(1), 481–495. https://doi.org/10.5198/jtlu.2022.2110
Huo, J., Yang, H., Li, C., Zheng, R., Yang, L., & Wen, Y. (2021). Influence of the built environment on e-scooter sharing ridership: A tale of five cities. Journal of Transport Geography, 93, 103084. https://doi.org/10.1016/j.jtrangeo.2021.103084
Institute of Transportation Engineers (ITE). (2023). Parking generation manual, 6th edition. Washington, DC: ITE.
James, O., Swiderski, J. I., Hicks, J., Teoman, D., & Buehler, R. (2019). Pedestrians and e-scooters: An initial look at e-scooter parking and perceptions by riders and non-riders. Sustainability, 11(20), 5591. https://doi.org/10.3390/su11205591
Jiang, Q., Ou, S.-J., & Wei, W. (2019). Why shared bikes of free-floating systems were parked out of order? A preliminary study based on factor analysis. Sustainability, 11(12), 3287. https://doi.org/10.3390/su11123287
Jiao, J., & Bai, S. (2020). Understanding the shared e-scooter travels in Austin, TX. ISPRS International Journal of Geo-Information, 9(2), 135. https://doi.org/10.3390/ijgi9020135
Keane, J. (2023). Paris’ e-scooter ban has come into effect. Forbes. Retrieved from https://www.forbes.com/sites/jonathankeane/2023/09/01/paris-e-scooter-ban-has-come-into-effect/
Klein, N., Brown, A., & Thigpen, C. (2023). Clutter and compliance: Scooter parking interventions and perceptions. Active Travel Studies, 3(1). https://doi.org/10.16997/ats.1196
Mangold, M., Zhao, P., Haitao, H., & Mansourian, A. (2022). Geo-fence planning for dockless bike-sharing systems: A GIS-based multi-criteria decision analysis framework. Urban Informatics, 1(1), 17. https://doi.org/10.1007/s44212-022-00013-1
Moran, M. E., Laa, B., & Emberger, G. (2020). Six scooter operators, six maps: Spatial coverage and regulation of micromobility in Vienna, Austria. Case Studies on Transport Policy, 8(2), 658–671. https://doi.org/10.1016/j.cstp.2020.03.001
Murphy, A., & Haydu, B. (2023, March 2). Right-sizing micromobility parking: How much parking does your city need? Lime Micromobility. Retrieved from https://www.li.me/blog/right-sizing-micromobility-parking-how-much-parking-does-your-city-need
Nabizad, M. (2021). DC law now requires riders to lock shared electric scooters to bike racks, scooter corrals, or signposts after use. Retrieved from https://ddot.dc.gov/release/dc-law-now-requires-riders-lock-shared-electric-scooters-bike-racks-scooter-corrals-or
National Association of City Transportation Officials (NACTO). (2022). Shared micromobility in the U.S. 2020-2021. New York: National Association of City Transportation Officials. https://nacto.org/shared-micromobility-2020-2021/
Nivel. (2023). Test report—Scooter parking compliance Bergen. Google Docs. Retrieved from https://drive.google.com/file/d/1zrL7S9zU97Cu9L2uO-33HpxAkXZ7Qe-N/view
Owen, N., Cerin, E., Leslie, E., duToit, L., Coffee, N., Frank, L. D., …, & Sallis, J. F. (2007). Neighborhood walkability and the walking behavior of Australian adults. American Journal of Preventive Medicine, 33(5), 387–395. https://doi.org/10.1016/j.amepre.2007.07.025
Ramm, F. (2022). OpenStreetMap data in layered GIS-format. Retrieved from https://download.geofabrik.de/osm-data-in-gis-formats-free.pdf
Reck, D. J., Martin, H., & Axhausen, K. W. (2022). Mode choice, substitution patterns and environmental impacts of shared and personal micro-mobility. Transportation Research Part D: Transport and Environment, 102, 103134. https://doi.org/10.1016/j.trd.2021.103134
Saelens, B. E., & Handy, S. L. (2008). Built environment correlates of walking: A review. Medicine and Science in Sports and Exercise, 40(7 Suppl), S550–S566. https://doi.org/10.1249/MSS.0b013e31817c67a4
Sahr, K., White, D., & Kimerling, A. J. (2003). Geodesic discrete global grid systems. Cartography and Geographic Information Science, 30(2), 121–134. https://doi.org/10.1559/152304003100011090
Schiavina, M., Freire, S., Carioli, A., & MacManus, K. (2023). GHS-POP R2023A - GHS population grid multitemporal (1975-2030) [Dataset]. Retrieved from https://doi.org/10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE
Shaji, N., Andrade, T., Ribeiro, R. P., & Gama, J. (2022). Study on correlation between vehicle emissions and air quality in Porto. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 181–196.
Si, H., Liang, J., Ke, J., Cheng, L., & De Vos, J. (2024). What limits improper bike-sharing parking most: Penalties or incentives? Findings from an online behavioral experiment. Transportation Research Part F: Traffic Psychology and Behavior, 107, 133–148. https://doi.org/10.1016/j.trf.2024.09.001
Šidlovský, M., & Ravas, F. (2023). Building knowledge graph in the transportation domain. In 2023 Smart City Symposium Prague (SCSP), 1–4.
Tao, T., Wang, J., & Cao, X. (2020). Exploring the non-linear associations between spatial attributes and walking distance to transit. Journal of Transport Geography, 82, 102560. https://doi.org/10.1016/j.jtrangeo.2019.102560
Tao, T., Wu, X., Cao, J., Fan, Y., Das, K., & Ramaswami, A. (2020). Exploring the nonlinear relationship between the built environment and active travel in the Twin Cities. Journal of Planning Education and Research, 0739456X20915765. https://doi.org/10.1177/0739456X20915765
Thigpen, C. (2019, January 14). Latest data show lime attracts new riders to active transportation, reduces car use and more. Lime micromobility. The Lime Times. Retrieved from https://www.li.me/blog/latest-data-lime-attracts-new-riders-reduces-car-use-more
Waerden, P. van der, Timmermans, H., & de Bruin-Verhoeven, M. (2017). Car drivers’ characteristics and the maximum walking distance between parking facility and final destination. Journal of Transport and Land Use, 10(1), 1–11. https://doi.org/10.5198/jtlu.2015.568
Wang, Y., Jia, S., Zhou, H., Charlton, S., & Hazen, B. (2021). Factors affecting orderly parking of dockless shared bicycles: An exploratory study. International Journal of Logistics Research and Applications, 24(2), 103–125. https://doi.org/10.1080/13675567.2020.1727424
Woźniak, S., & Szymański, P. (2021). Hex2vec: Context-aware embedding H3 hexagons with OpenStreetMap tags. In Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, 61–71.
Yang, H., Zheng, R., Li, X., Huo, J., Yang, L., & Zhu, T. (2022). Nonlinear and threshold effects of the built environment on e-scooter sharing ridership. Journal of Transport Geography, 104, 103453. https://doi.org/10.1016/j.jtrangeo.2022.103453

