From shared micro-mobility to shared responsibility: Using crowdsourcing to understand dockless vehicle violations in Austin, Texas

References

• Ajao, A. (2019). Electric scooters and micro-mobility: Here’s everything you need to know. Forbes. https://www.forbes.com/sites/adeyemiajao/2019/02/01/everything-you-want-to-know-about-scooters-and-micro-mobility/
   Google Scholar
• Allem, J.-P., & Majmundar, A. (2019). Are electric scooters promoted on social media with safety in mind? A case study on Bird’s Instagram. Preventive Medicine Reports, 13, 62–63. https://doi.org/10.1016/j.pmedr.2018.11.013
   PubMedGoogle Scholar
• Anselin, L. (2010). Local Indicators of Spatial Association-LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
   Web of Science ®Google Scholar
• Austin. (2018). Director rules for deployment and operation of shared small vehicle mobility systems. https://austintexas.gov/docklessmobility
   Google Scholar
• Bai, S., & 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, 20, 264–272. https://doi.org/10.1016/j.tbs.2020.04.005
   Web of Science ®Google Scholar
• Blake, M. J. F. (1967). Time of day effects on performance in a range of tasks. Psychonomic Science, 9(6), 349–350. https://doi.org/10.3758/BF03327842
   Google Scholar
• Bott, M., & Young, G. (2012). The role of crowdsourcing for better governance in international development. The Fletcher Journal of Human Security, XXVII, 47–70. https://www.semanticscholar.org/paper/The-Role-of-Crowdsourcing-for-Better-Governance-in-Bott-Young/2b4b9e693aa12ecf66635bd2c7b080d340d90513
   Google Scholar
• Brabham, D. C. (2008). Crowdsourcing as a model for problem solving: An introduction and cases. Convergence: The International Journal of Research into New Media Technologies, 14(1), 75–90. https://doi.org/10.1177/1354856507084420
   Google Scholar
• Brabham, D. C. (2012). Motivations for participation in a crowdsourcing application to improve public engagement in transit planning. Journal of Applied Communication Research, 40(3), 307–328. https://doi.org/10.1080/00909882.2012.693940
   Web of Science ®Google Scholar
• Brabham, D. C., Ribisl, K. M., Kirchner, T. R., & Bernhardt, J. M. (2014). Crowdsourcing applications for public health. American Journal of Preventive Medicine, 46(2), 179–187. https://doi.org/10.1016/j.amepre.2013.10.016
   PubMed Web of Science ®Google Scholar
• Brereton, R. G. (2019). Introduction to analysis of variance: ANOVA. Journal of Chemometrics, 33(1), e3018. https://doi.org/10.1002/cem.3018
   Web of Science ®Google Scholar
• Chen, C., Ma, J., Susilo, Y., Liu, Y., & Wang, M. (2016). The promises of big data and small data for travel behavior (aka human mobility) analysis. Transportation Research Part C: Emerging Technologies, 68, 285–299. https://doi.org/10.1016/j.trc.2016.04.005
   PubMed Web of Science ®Google Scholar
• Davidoff, P. (1965). Advocacy and pluralism in planning. Journal of the American Institute of Planners, 31(4), 331–338. https://doi.org/10.1080/01944366508978187
   Web of Science ®Google Scholar
• Fainstein, S. S. (2000). New directions in planning theory. Urban Affairs Review, 35(4), 28. https://doi.org/10.1177/107808740003500401
   Web of Science ®Google Scholar
• Feir-Walsh, B. J., & Toothaker, L. E. (1974). An empirical comparison of the Anova F-test, normal scores test and Kruskal-Wallis test under violation of assumptions. Educational and Psychological Measurement, 34(4), 789–799. https://doi.org/10.1177/001316447403400406
   Web of Science ®Google Scholar
• Forester, J. (1994). Bridging interests and community: Advocacy planning and the challenges of deliberative democracy. Journal of the American Planning Association, 60(2), 153–158. https://doi.org/10.1080/01944369408975567
   Web of Science ®Google Scholar
• Goldstein, D., Hahn, C. S., Hasher, L., Wiprzycka, U. J., & Zelazo, P. D. (2007). Time of day, intellectual performance, and behavioral problems in Morning versus Evening type adolescents: Is there a synchrony effect? Personality and Individual Differences, 42(3), 431–440. https://doi.org/10.1016/j.paid.2006.07.008
   PubMed Web of Science ®Google Scholar
• Graham-Rowe, E., Skippon, S., Gardner, B., & Abraham, C. (2011). Can we reduce car use and, if so, how? A review of available evidence. Transportation Research Part A: Policy and Practice, 45(5), 401–418. https://doi.org/10.1016/j.tra.2011.02.001
   Web of Science ®Google Scholar
• Griffin, G. P., & Jiao, J. (2015). Where does bicycling for health happen? Analyzing volunteered geographic information through place and plexus. Journal of Transport & Health, 2(2), 238–247. https://doi.org/10.1016/j.jth.2014.12.001
   Web of Science ®Google Scholar
• Griffin, G. P., & Jiao, J. (2019a). The geography and equity of crowdsourced public participation for active transportation planning. Transportation Research Record: Journal of the Transportation Research Board, 2673(1), 460–468. https://doi.org/10.1177/0361198118823498
   Web of Science ®Google Scholar
• Griffin, G. P., & Jiao, J. (2019b). Crowdsourcing bike share station locations: Evaluating participation and placement. Journal of the American Planning Association, 85(1), 35–48. https://doi.org/10.1080/01944363.2018.1476174
   Web of Science ®Google Scholar
• Gu, T., Kim, I., & Currie, G. (2019). To be or not to be dockless: Empirical analysis of dockless bikeshare development in China. Transportation Research Part A: Policy and Practice, 119, 122–147. https://doi.org/10.1016/j.tra.2018.11.007
   Web of Science ®Google Scholar
• Hanna, K. S. (2000). The paradox of participation and the hidden role of information: A case study. Journal of the American Planning Association, 66(4), 398–410. https://doi.org/10.1080/01944360008976123
   Web of Science ®Google Scholar
• Hecke, T. V. (2012). Power study of ANOVA versus Kruskal-Wallis test. Journal of Statistics and Management Systems, 15(2–3), 241–247. https://doi.org/10.1080/09720510.2012.10701623
   Google Scholar
• Hou, Q., Zhang, X., Li, B., Zhang, X., & Wang, W. (2019). Identification of low-carbon travel block based on GIS hotspot analysis using spatial distribution learning algorithm. Neural Computing and Applications, 31(9), 4703–4713. https://doi.org/10.1007/s00521-018-3447-8
   Web of Science ®Google Scholar
• James, O., Swiderski, J., 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
   Web of Science ®Google Scholar
• Jestico, B., Nelson, T., & Winters, M. (2016). Mapping ridership using crowdsourced cycling data. Journal of Transport Geography, 52, 90–97. https://doi.org/10.1016/j.jtrangeo.2016.03.006
   Web of Science ®Google Scholar
• Jiao, J., & Bai, S. (2020). Understanding the shared E-scooter travels in Austin, TX. ISPRS International Journal of Geo-Information, 9(2), 135–147. https://doi.org/10.3390/ijgi9020135
   Web of Science ®Google Scholar
• Lerman, A. E., & Weaver, V. (2014). Staying out of sight? Concentrated policing and local political action. The ANNALS of the American Academy of Political and Social Science, 651(1), 202–219. https://doi.org/10.1177/0002716213503085
   Web of Science ®Google Scholar
• Levine, J. R., & Gershenson, C. (2014). From political to material inequality: Race, immigration, and requests for public goods. Sociological Forum, 29(3), 607–627. https://doi.org/10.1111/socf.12106
   Web of Science ®Google Scholar
• McArthur, D. P., & Hong, J. (2019). Visualising where commuting cyclists travel using crowdsourced data. Journal of Transport Geography, 74, 233–241. https://doi.org/10.1016/j.jtrangeo.2018.11.018
   Web of Science ®Google Scholar
• McNally, M. G. (2000). The four step model. UC Irvine: Center for Activity Systems Analysis, 19. https://escholarship.org/uc/item/7j0003j0
   Google Scholar
• McNally, M. G., & Kulkarni, A. (1997). Assessment of influence of land use–transportation system on travel behavior. Transportation Research Record, 1607(1), 105–115. https://doi.org/10.3141/1607-15
   Google Scholar
• Mead, N. V. (2017). Uber for bikes: How “dockless” cycles flooded China–And are heading overseas. The Guardian. https://www.theguardian.com/cities/2017/mar/22/bike-wars-dockless-china-millions-bicycles-hangzhou
   Google Scholar
• Musakwa, W., & Selala, K. M. (2016). Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa. Data in Brief, 9, 898–905. https://doi.org/10.1016/j.dib.2016.11.002
   PubMed Web of Science ®Google Scholar
• NACTO. (2019). Shared micro-mobility in the U.S.: 2018. National Association of City Transportation Officials. https://nacto.org/wp-content/uploads/2019/04/NACTO_Shared-Micromobility-in-2018_Web.pdf
   Google Scholar
• O’Brien, D. T., Sampson, R. J., & Winship, C. (2015). Ecometrics in the age of big data: Measuring and assessing “broken windows” using large-scale administrative records. Sociological Methodology, 45(1), 101–147. https://doi.org/10.1177/0081175015576601
   Web of Science ®Google Scholar
• Özdemir, E., & Tasan-Kok, T. (2019). Planners’ role in accommodating citizen disagreement: The case of Dutch urban planning. Urban Studies, 56(4), 741–759. https://doi.org/10.1177/0042098017726738
   Web of Science ®Google Scholar
• Özkazanç, S., & Özdemir Sönmez, F. N. (2017). Spatial analysis of social exclusion from a transportation perspective: A case study of Ankara metropolitan area. Cities, 67, 74–84. https://doi.org/10.1016/j.cities.2017.04.013
   Web of Science ®Google Scholar
• Radil, S. M., & Jiao, J. (2016). Public participatory GIS and the geography of inclusion. The Professional Geographer, 68(2), 202–210. https://doi.org/10.1080/00330124.2015.1054750
   Web of Science ®Google Scholar
• Rahim Taleqani, A., Hough, J., & Nygard, K. E. (2019). Public opinion on dockless bike sharing: A machine learning approach. Transportation Research Record: Journal of the Transportation Research Board, 2673(4), 195–204. https://doi.org/10.1177/0361198119838982
   Web of Science ®Google Scholar
• Reuters. (2018). Bike-sharing firm Ofo’s dramatic fall from grace a warning to China’s tech investors. South China Morning Post. https://www.scmp.com/tech/start-ups/article/2179485/bike-sharing-firm-ofos-dramatic-fall-grace-warning-chinas-tech
   Google Scholar
• Ruxton, G. D., & Beauchamp, G. (2008). Some suggestions about appropriate use of the Kruskal–Wallis test. Animal Behaviour, 76(3), 1083–1087. https://doi.org/10.1016/j.anbehav.2008.04.011
   Web of Science ®Google Scholar
• Shaheen, S., & Cohen, A. (2019). Shared micromobility policy toolkit: Docked and dockless bike and scooter sharing. UC Berkeley Transportation Sustainability Research Center. https://escholarship.org/uc/item/00k897b5
   Google Scholar
• Smith, A. (2015). Crowdsourcing for active transportation. Institute of Transportation Engineers Journal, 85(5), 30–36. https://search.proquest.com/docview/1682435382?pqorigsite=gscholar&fromopenview=true
   Web of Science ®Google Scholar
• Tu, Y., Chen, P., Gao, X., Yang, J., & Chen, X. (2019). How to make dockless bikeshare good for cities: Curbing oversupplied bikes. Transportation Research Record: Journal of the Transportation Research Board, 2673(6), 618–627. https://doi.org/10.1177/0361198119837963
   Web of Science ®Google Scholar
• Van Herzele, A. (2004). Local knowledge in action: Valuing nonprofessional reasoning in the planning process. Journal of Planning Education and Research, 24(2), 197–212. https://doi.org/10.1177/0739456X04267723
   Web of Science ®Google Scholar
• Vargha, A., & Delaney, H. (1998). The Kruskal-Wallis test and stochastic homogeneity. Journal of Educational and Behavioral Statistics, 23(2), 170–192. https://doi.org/10.3102/10769986023002170
   Web of Science ®Google Scholar
• White, A., & Trump, K.-S. (2018). The promises and pitfalls of 311 data. Urban Affairs Review, 54(4), 794–823. https://doi.org/10.1177/1078087416673202
   Web of Science ®Google Scholar
• Yin, J., Qian, L., & Shen, J. (2019). From value co-creation to value co-destruction? The case of dockless bike sharing in China. Transportation Research Part D: Transport and Environment, 71, 169–185. https://doi.org/10.1016/j.trd.2018.12.004
   Web of Science ®Google Scholar
• Zarif, R., Pankratz, D., & Kelman, B. (2019). Small is beautiful: Making micromobility work for citizens, cities, and service providers. Deloitte Insights. https://www2.deloitte.com/us/en/insights/focus/future-of-mobility/micro-mobility-is-the-future-of-urban-transportation.html?id=us:2ps:3gl:confidence:eng:cons:42319:nonem:na:nhRV7UOl:1149484916:344865936403:b:Future_of_Mobility:Micromobility_BMM:nb
   Google Scholar