Measuring accessibility: A Big Data perspective on Uber service waiting times
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Abstract
This study aims to relate information about the waiting times of ride-sourcing services, with specific reference to Uber, using socioeconomic variables from São Paulo, Brazil. The intention is to explore the possibility of using this measure as an accessibility proxy. A database was created with the mean waiting time data per district, which was aggregated to a set of socioeconomic and transport infrastructure variables. From this database, a multiple linear regression model was built. In addition, the stepwise method selected the most significant variables. Moran’s I test confirmed the spatial distribution pattern of the measures, motivating the use of a spatial autoregressive model. The results indicate that physical variables, such as area and population density, are important to explain this relation. However, the mileage of district bus lines and the non-white resident rate were also significant. Besides, the spatial component indicates a possible relation to accessibility.
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References
Aranha, V. (2005). Mobilidade pendular na metrópole paulista. São Paulo em Perspectiva, 19(4), 96-109. doi:10.1590/S0102- 88392005000400006
Batterham, A. M., Tolfrey, K., George, K. P. (1997). Nevill’s explanation of Kleiber’s 0.75 mass exponent: An artifact of collinearity problems in least squares models? Journal of Applied Physiology, 82(2), 693-697.
doi:10.1152/jappl.1997.82.2.693
Cohen, P., Metcalfe, R., Angrist, J., Chen, K., Doyle, J., Farber, H., Hahn, R. (2016). Using big data to estimate consumer surplus: The case of Uber. National Bureau of Economic Research (Working paper nº22627). Retrieved from https://www.nber.org/papers/w22627 doi:
3386/w22627
Flores, O., & Rayle, L. (2017). How cities use regulation for innovation:The case of Uber, Lyft and Sidecar in San Francisco. Transportation Research Procedia, 25, 3756-3768. doi:10.1016/j.trpro.2017.05.232
Francisco, E. de R. (2010). Indicadores de renda baseados em consumo de energia elétrica: abordagens domiciliar e regional na perspectiva da estatística espacial (Doctoral Thesis, Fundação Getulio Vargas). Retrieved from https://bibliotecadigital.fgv.br/dspace/handle/10438/8158
Hair, J., Babin, B., Money, A., & Samouel, P. (2005). Fundamentos de métodos de pesquisa em administração. Porto Alegre, RS: Bookman Companhia Ed.
Hall, J. V, Horton, J. J., & Knoepfle, D. T. (2019). Pricing efficiently in designed markets: The Case of Ride-Sharing. 1-76. Retrieved from
http://john-joseph-horton.com/papers/uber_price.pdf
Hall, J. V, & Krueger, A. B. (2016). An analysis of the labor market for Uber’s driver-partners in the United States. In National Bureau of Economic Research (Working paper nº 22843). Retrieved from https://www.nber.org/papers/w22843. doi:10.3386/w22843.
Hansen, W. G. (1959). How accessibility shapes land use.
Journal of the American Institute of planners, 25(2), 73-76.
doi:10.1080/01944365908978307
Hughes, R., & MacKenzie, D. (2016). Transportation network company wait times in Greater Seattle, and relationship to socioeconomic indicators. Journal of Transport Geography, 56, 36-44. doi:10.1016/j.jtrangeo.2016.08.014
Instituto Brasileiro de Geografia e Estatística. (2010). Censo
demográfico. Notas Metodológicas–Microdados da Amostra. Rio de Janeiro. Retrieved from ftp://ftp.ibge.gov.br/Censos/Censo_
Demografico_2010/Resultados_Gerais_da_Amostra/Microdados/Documentacao.zip
Jin, S. T., Kong, H., Wu, R., & Sui, D. Z. (2018). Ridesourcing , the sharing economy , and the future of cities. Cities, 76(January), 96-104. doi:10.1016/j.cities.2018.01.012
Kim, B. G., Trimi, S., & Chung, J. (2014). Big-Data applications in the government sector. Communications of the ACM, 57(3), 78-85. doi:10.1145/2500873
Krajzewicz, D., Erdmann, J., Behrisch, M., & Bieker, L. (2012). Recent development and applications of SUMO-Simulation of Urban MObility. International Journal On Advances in Systems and Measurements, 5(3-4), 128-138.
Kwan, M. (1998). Space-time and integral measures of individual accessibility: A comparative analysis using a point-based framework. Geographical Analysis, 30(3), 191-216. doi:10.1111/j.1538-4632.1998.tb00396.x
Kwan, M. P. (2016). Algorithmic geographies: Big data, algorithmic uncertainty, and the production of geographic knowledge. Annals of the American Association of eographers, 106(2), 274-282. doi:10.1080/00045608.2015.1117937
Lessa, D. A., Lobo, C., & Cardoso, L. (2019). Accessibility and urban mobility by bus in Belo Horizonte / Minas Gerais – Brazil. Journal of Transport Geography, 77(May 2018), 1-10. doi:10.1016/j.jtrangeo.2019.04.004
Letouzé, E., & Jütting, J. (2015). Official statistics, Big Data and human development: towards a new conceptual and operational approach. Data Pop Alliance White Paper Series. Retrieved from https://paris21.org/sites/default/files/WPS_OfficialStatistics_June2015.pdf
Litman, T. (2003). Measuring transportation: Traffic mobility and accessibility. Victoria Transport Policy Institute. ITE Journal, 73(10), 28-32.
McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review. Retrieved from https://hbr.org/
Metrô. (2008). Pesquisa Origem-Destino 2007. Secretaria de Transportes Metropolitanos. Retrieved from http://www.metro.sp.gov.br/
pesquisa-od/arquivos/OD_2007_Sumario_de_Dados.pdf
Ministério do Trabalho (2016). Relação Anual de Informações Sociais - RAIS ano-base 2016. Portaria n. 1464 de 30 de Dez. 2016. Aprova Instruções Para a Declaração Da Relação Anual de Informações Sociais - RAIS Ano-Base 2016. Retrieved from http://www.rais.gov.br/sitio/sobre.jsf
Morandi, E., Ribeiro, R., Hernandes, E., Camara, B., Spinola, L., & Francisco, E. D. R. (2016). Análise Geoespacial da Relação entre Transporte Público sobre Trilhos, Renda e Tempo Médio de Deslocamento. Paper presented on XL EnANPAD, Salvador, BA. Retrieved from http://www.anpad.org.br/~anpad/eventos.
php?cod_evento=1&cod_edicao_subsecao=1302&cod_evento_
edicao=83&cod_edicao_trabalho=21023
Noulas, A., Scellato, S., Lambiotte, R., Pontil, M., & Mascolo, C. (2012). A Tale of many cities: Universal patterns in human urban mobility. PloS One, 7(9). doi:10.1371/annotation/ca85bf7a-7922-47d5-8bfbbcdf25af8c72
Páez, A., Scott, D. M., & Morency, C. (2012). Measuring accessibility: Positive and normative implementations of various accessibility indicators. Journal of Transport Geography, 25, 141–153. https://doi.
org/10.1016/j.jtrangeo.2012.03.016
Pääkkönen, P., & Pakkala, D. (2015). Reference architecture and classification of technologies, products and services for big data systems. Big data research, 2(4), 166-186. doi:10.1016/j.bdr.2015.01.001
Preston, V., & McLafferty, S. (1999). Spatial mismatch research in the 1990s: Progress and potential. Papers in Regional Science, 78(4), 387-402. doi:10.1111/j.1435-5597.1999.tb00752.x
Schwanen, T. (2016). Geographies of transport I: Reinventing
a field? Progress in Human Geography, 40(1), 126-137.
doi:10.1177/0309132514565725
Schwanen, T. (2017). Geographies of transport II: Reconciling the general and the particular. Progress in Human Geography, 41(3), 355-364. doi:10.1177/0309132516628259
Stelder, D. (2016). Regional accessibility trends in Europe: Road Infrastructure, 1957-2012. Regional Studies, 50(6), 983-995. doi:10.1080/00343404.2014.952721
Torres, H. da G., Marques, E., Ferreira, M. P., & Bitar, S. (2003). Pobreza e espaço: Padrões de segregação em São Paulo. Estudos Avançados,17(47), 97-128. doi:10.1590/S0103-40142003000100006
Tribby, C. P., & Zandbergen, P. A. (2012). High-resolution spatio-temporal modeling of public transit accessibility. Applied Geography Journal, 34, 345-–355. doi:10.1016/j.apgeog.2011.12.008
Wang, M., & Mu, L. (2018). Spatial disparities of Uber accessibility: An exploratory analysis in Atlanta, USA. Computers, Environment and Urban Systems, 67, 169-175. doi:10.1016/j.compenvurbsys.2017.09.003
Weibull, J. W. (1980). On the numerical measurement of accessibility. Environment and Planning A: Economy and Space, 12(1), 53-67.doi:10.1068/a120053
Zhou, X., Wang, M., & Li, D. (2017). From stay to play–A travel planning tool based on crowdsourcing user-generated contents. Applied Geography, 78, 1-11. doi:10.1016/j.apgeog.2016.10.002