Abstract
Despite the growing popularity and benefits of shared dockless e-scooters, there is controversy over whether this is an affordable travel mode for everyone. This paper explores the nonlinear relationship between shared dockless e-scooters and the location affordability of neighborhoods. By analyzing shared dockless e-scooter trip data collected between April 2019 and March 2020 from 1,886 census block groups in Los Angeles, we used a random forest model to investigate this nonlinear relationship. The variable importance plot revealed that economic variables (cost versus income) have greater explanatory power than other variables. In the partial dependency plots, neighborhoods spending more than 35% of their income on housing costs were more inclined to use e-scooters. On the other hand, when transportation represents more than 9% of household income, the e-scooter trip density decreases. Location affordability appears to be serving as a proxy for compactness, with compact areas having higher housing costs and lower transportation costs. The two together are lower in compact areas. The market for e-scooters is thus higher in compact areas where there are many potential users, trips are shorter, and users have more discretionary income due to lower h + t costs. The results of this study highlight the importance of location-specific planning in promoting the effective use of shared dockless e-scooters as a sustainable and active transportation mode beyond simply focusing on costs and incentive programs.
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References
Aarhaug, J., Fearnley, N., Hartveit, K.J.L., Johnsson, E.: Price and competition in emerging shared e-scooter markets. Res. Transp. Econ. 98 (2023). https://doi.org/10.1016/j.retrec.2023.101273
Abouelela, M., Haddad, A.C., Antoniou, C.: Are young users willing to shift from carsharing to scooter-sharing? Transp. Res. PART D-TRANSPORT Environ. 95 (2021). https://doi.org/10.1016/j.trd.2021.102821
Acevedo-Garcia, D., McArdle, N., Hardy, E., Dillman, K.-N.N., Reece, J., Crisan, U.I., Norris, D., Osypuk, T.L.: Neighborhood opportunity and location affordability for low-income renter families. Hous. Policy Debate. 26(4–5), 607–645 (2016). https://doi.org/10.1080/10511482.2016.1198410
Badia, H., Jenelius, E.: Shared e-scooter micromobility: Review of use patterns, perceptions and environmental impacts. Transp. Reviews. (2023). https://doi.org/10.1080/01441647.2023.2171500
Bai, S., Jiao, J.: Dockless e-scooter usage patterns and urban built environments: A comparison study of Austin, TX, and Minneapolis, M.N. Travel Behav. Soc. 20, 264–272 (2020). https://doi.org/10.1016/j.tbs.2020.04.005
Basu, R., Ferreira, J.: Planning car-lite neighborhoods: Does bikesharing reduce auto-dependence? Transp. Res. Part D: Transp. Environ. 92(February), 102721 (2021). https://doi.org/10.1016/j.trd.2021.102721
Bieri, D.S.: Housing affordability BT - encyclopedia of quality of life and well-being research (A. C. Michalos, Ed.; pp. 2971–2975). Springer Netherlands. (2014). https://doi.org/10.1007/978-94-007-0753-5_1329
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Caspi, O., Smart, M.J., Noland, R.B.: Spatial associations of dockless shared e-scooter usage. Transp. Res. D Transp. Environ. 86. (2020). https://doi.org/10.1016/j.trd.2020.102396
Center for Neighborhood Technology: H + T Index methods (issue March) (2015). https://htaindex.cnt.org/about/HT_Index_Methods_2013.pdf
City of Santa Monica:. Shared Mobility Pilot Program. November 2019, 1–57. (2019)
Clark, M.: Generlized Additive Model. (2022). https://m-clark.github.io/generalized-additive-models/references.html
Currans, K. M., Iroz-Elardo, N., Ewing, R., Choi, D., Siracuse, B., Lyons, T., Fitzpatrick, Q., Griffee, J.: Scooting to a new era in active transportation: examining the use and safety of e-scooters. Research report #NITC-RR-1281. Portland, OR: National Institute for Transportation and Communities (NITC) (2022). https://doi.org/10.15760/trec.272
Ewing, R., Cervero, R.: Travel and the built environment. J. Am. Plann. Association. 76(3), 265–294 (2010). https://doi.org/10.1080/01944361003766766
Fan, A., Chen, X., Wan, T.: How have travelers changed mode choices for first/last mile trips after the introduction of bicycle-sharing systems: an empirical study in Beijing, China. J. Adv. Transp. 2019 (2019). https://doi.org/10.1155/2019/5426080
Fearnley, N., Johnsson, E., Berge, S.H.: Patterns of e-scooter use in combination with public transport. Findings. 24(6), 85–89 (2020). https://doi.org/10.32866/001c.13707
Fedorowicz, M., Bramhall, E., Ezike, R.: New ,mobility and equity: Insights for medium-size cities. July, 49 (2020)
Goldsmith, S., Leger, M.: Effectively managing connected mobility marketplaces. SSRN Electron. J. (2020). https://doi.org/10.2139/ssrn.3544294
Greene, S., Macdonald, G., Arena, O., Srini, T., Gourevitch, R., Ezike, R., Stern, A.: Technology and Equity in Cities Emerging Challenges and Opportunities. November. (2019)
Haas, P.M., Newmark, G.L., Morrison, T.R.: Untangling housing cost and transportation interactions: The Location Affordability Index Model—Version 2 (LAIM2). Hous. Policy Debate. 26(4–5), 568–582 (2016). https://doi.org/10.1080/10511482.2016.1158199
Hamidi, S., Ewing, R.: Is sprawl affordable for americans? Exploring the association between housing and transportation affordability and urban sprawl. Transp. Res. Rec. 2500(2500), 75–79 (2015). https://doi.org/10.3141/2500-09
Hartell, A.M.: Evaluating the Concept of Location Affordability: Recent data on the Relationship between Transportation, Housing, and Urban Form. Hous. Policy Debate. 27(3), 356–371 (2017). https://doi.org/10.1080/10511482.2016.1220402
Hartell, A.M.: Is performance measurement improving planning practice? The case of Location Affordability in Long-Range Transportation Plans. Transp. Res. Rec. 2672(51), 31–44 (2018). https://doi.org/10.1177/0361198118776154
Hosseinzadeh, A., Algomaiah, M., Kluger, R., & Li, Z.: E-scooters and sustainability: Investigating the relationship between the density of E-scooter trips and characteristics of sustainable urban development. Sustainable Cities and Society, 66(December 2020), 102624. https://doi.org/10.1016/j.scs.2020.102624
Huo, J.H., Yang, H.T., Li, C.J., Zheng, R., Yang, L.C.A., Wen, Y.: Influence of the built environment on E-scooter sharing ridership: A tale of five cities. J. Transp. Geogr. 93 (2021). https://doi.org/10.1016/j.jtrangeo.2021.103084 N.PAG-N.PAG
LA Metro:. Metro: Bus, Rail, Subway, Bike & Micro in Los Angeles. (2023). https://www.metro.net/%0A
LADOT:. Year One Snapshot: A Review of the 2019–2020 Dockless Vehicle Pilot Program (Issue July). (2020). https://ladot.lacity.org/sites/default/files/documents/ladot-dockless-year-one-report.pdf
Lang, N., Schellong, D., Hagenmaier, M., Herrmann, A., Hohenreuther, M.: Putting Micromobility at the Center of Urban Mobility. Boston Consulting Group (2022). https://www.bcg.com/publications/2022/the-future-of-urban-mobility
Lantz, B.: Machine Learning with R. In Packt Publishing Ltd (Second Edi). Packt Publishing. (2015). https://doi.org/10.1007/978-981-10-6808-9
NACTO, .S.:. Shared Micromobility in the U: 2019. In National Association of City Transportation Officials. (2020)
NACTO:. Shared Micromobility in The U.S. 2020–2021. (2022)
Oeschger, G., Carroll, P., Caulfield, B.: Micromobility and public transport integration: The current state of knowledge. Transp. Res. Part D: Transp. Environ. 89(November) (2020). https://doi.org/10.1016/j.trd.2020.102628
Portland Bureau of Transportation:. Shared Electric Scooter Pilot. 36. (2019)
Qian, X., Jaller, M., Niemeier, D.: Enhancing equitable service level: Which can address better, dockless or dock-based Bikeshare systems? Journal of Transport Geography, 86. (2020). https://doi.org/10.1016/j.jtrangeo.2020.102784
Schmitt, A.: Why Isn’t Bike-Share Reaching More Low-Income People? Streetsblog USA. (2012). https://usa.streetsblog.org/2012/10/03/why-isnt-bike-share-reaching-more-low-income-people/
Shaheen, S., Bell, C., Cohen, A., Yelchuru, B.: Travel Behavior: Shared Mobility and Transportation Equity. In U.S. Department of Transportation. (2017)
Singer, M.E.: How affordable are accessible locations? Neighborhood affordability in U.S. urban areas with intra-urban rail service. Cities, 116(August 2020), 103295. (2021). https://doi.org/10.1016/j.cities.2021.103295
Smart, M.J., Klein, N.J.: Complicating the story of location affordability. Hous. Policy Debate. 28(3), 393–410 (2018). https://doi.org/10.1080/10511482.2017.1371784
Tian, G., Park, K., Ewing, R., Watten, M., Walters, J.: Traffic generated by mixed-use Developments—A follow-up 31-region study. Transp. Res. Part D: Transp. Environ. 78, 102205 (2020). https://doi.org/10.1016/j.trd.2019.102205
US Census Bureau:. U.S. Census Bureau quickfacts: Los Angeles City, California. (2021). https://www.census.gov/quickfacts/losangelescitycalifornia%0A
Wang, K., Qian, X., Fitch, D.T., Lee, Y., Malik, J., Circella, G.: What travel modes do shared e-scooters displace? A review of recent research findings. Transp. Reviews. (2022). https://doi.org/10.1080/01441647.2021.2015639
Yang, H., Huo, J., Bao, Y., Li, X., Yang, L., Cherry, C.R.: Impact of e-scooter sharing on bike sharing in Chicago. Transp. Res. Part A: Policy Pract. 154, 23–36 (2021). https://doi.org/10.1016/j.tra.2021.09.012
Yang, H., Zheng, R., Li, X., Huo, J., Yang, L., Zhu, T.: Nonlinear and threshold effects of the built environment on e-scooter sharing ridership. J. Transp. Geogr. 104(June), 103453 (2022). https://doi.org/10.1016/j.jtrangeo.2022.103453
Younes, H., Zou, Z.P., Wu, J.H., Baiocchi, G.: Comparing the Temporal Determinants of Dockless Scooter-share and Station-based Bike-share in Washington, D.C. Transportation Research Part A: Policy and Practice, 134(August 2019), 308–320. (2020). https://doi.org/10.1016/j.tra.2020.02.021
Zack, R.: Micromobilityʻs Opportunity to Serve the Underserved Edges. Issue September (2018). http://www.pewinternet.org/fact-sheet/mobile/
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This research was funded by the Graduate Research Fellowship of the graduate school at the University of Utah.
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WY: Conceptualization, Methodology, Data curation, Formal analysis, Writing-Original draft preparation, Visualization. RE: Review & Editing. All the authors reviewed the results and approved the final version of the manuscript.
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Yang, W., Ewing, R. Examining the nonlinear effects of neighborhood housing + transportation affordability on shared dockless e-scooter trips using machine learning approach. Transportation (2023). https://doi.org/10.1007/s11116-023-10448-3
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DOI: https://doi.org/10.1007/s11116-023-10448-3