Abstract
The burst of demand for transportation network companies (TNCs) such as Uber, Lyft, or Via, has significantly changed the transportation landscape and dramatically disrupted the Vehicle for Hire market that used to be dominated by taxicabs for many years. Since first being introduced by Uber in 2009, ridesourcing services have rapidly penetrated the market. This paper aims to investigate temporal and spatial patterns in taxi and TNC usage based on data at the taxi zone level in New York City. Similar analysis is possible using rideshare data that is specific to different countries. To our data, we fit suitable time series models to estimate the temporal patterns. Next, we subtract out the temporal effects and investigate spatial dependence in the residuals using global and local Moran’s I statistics. We discuss the relation between the spatial correlations and the demographic and land use effects at the taxi zone level. Estimating and removing these effects via a multiple linear regression model and recomputing the Moran’s I statistics on the resulting residuals enables us to investigate spatial dependence after accounting for these effects. Our analysis indicates interesting patterns in spatial correlations between taxi zones in NYC and over time, indicating that predictive modeling of ridesourcing usage benefits from accommodating both temporal and spatial dependence.
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References
Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27(2):93–115
Bivand RS, Wong DWS (2018) Comparing implementations of global and local indicators of spatial association. TEST 27:716–748
Erhardt GD, Roy S, Cooper D, Sana B, Chen M, Castiglione J (2019) Do transportation network companies decrease or increase congestion? Sci Adv. https://doi.org/10.1126/sciadv.aau2670
Fischer-Baum R. Bialik C (2015) Uber is taking millions of Manhattan rides away from taxis. Retrieved May 2020, https://fivethirtyeight.com/features/uber-is-taking-millions-of-manhattan-rides-away-from-taxis/
Gerte R, Konduri KC, Ravishanker N, Mondal A, Eluru N (2019) Understanding the relationships between demand for shared ride modes: case study using Open Data from New York City. Transp Res Rec. https://doi.org/10.1177/0361198119849584
Gimond M (2019) Intro to GIS and spatial analysis. Retrieved March 11, from https://mgimond.github.io/Spatial/index.html
Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: the forecast package for R. J Stat Softw 26(3):1–22
NYC Taxi & Limousine Commission (TLC) [Data File] (2017). TLC Trip Data. http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml
NYC Open Data [Data File] (2017) NYC City Permitted Event, Holiday Data https://opendata.cityofnewyork.us/
National Oceanic and Atmospheric Administration (NOAA) [Data File] (2017). NYC Weather Data. https://www.noaa.gov/weather
Poulsen LK, Dekkers D, Wagenaar N, Snijders W, Lewinsky B, Mukkamala RR, Vatrapu R (2016) Green cabs vs. uber in new york city. In: Big Data (BigData Congress), IEEE International Congress, pp 222–229
Qian X, Lei T, Xue J, Lei Z, Ukkusuri SV (2020) Impact of transportation network companies on urban congestion: evidence from large-scale trajectory data. Sustain Urban Areas 55:102053
Rayle L et al (2016) Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco. Transp Policy 45:168–178
Schaller B (2017) Unsustainable? The growth of App-based ride services and traffic, travel and the future of New York City. Schaller Consulting
Schaller B. (2018) The new automobility: Lyft, Uber and the future of American cities. Schaller Consulting.
Schneider T (2018) Analyzing 1.1 Billion NYC Taxi and Uber trips, with a vengeance. An open-source exploration of the city’s neighborhoods, nightlife, airport traffic, and more, through the lens of publicly available taxi and Uber data. https://toddwschneider.com/posts/analyzing-1-1-billion-nyc-taxi-and-uber-trips-with-a-vengeance/#update-2017.
Shumway RH, Stoffer DS (2017) Time series analysis and its applications, 4th edn. New York, Springer
Silver N, Fischer-Baum R (2014) Public transit should be Uber’s new best friend. The two combined could take personal cars off city streets. https://fivethirtyeight.com/features/public-transit-should-be-ubers-new-best-friend/.
Venables WN, Ripley BD (2002) Modern Applied Statistics with S, 4th edn. Springer, New York. https://www.stats.ox.ac.uk/pub/MASS4/
Acknowledgements
The authors are grateful to the Center for Advanced Multimodal Mobility Solutions and Education Year 3 funding for supporting this research and to Raymond Gerte for his help with data preparation. The authors also thank the reviewers for their suggestions that helped improve the paper.
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Toman, P., Zhang, J., Ravishanker, N. et al. Spatiotemporal Analysis of Ridesourcing and Taxi Usage by Zones. J Indian Soc Probab Stat 22, 231–249 (2021). https://doi.org/10.1007/s41096-021-00102-5
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DOI: https://doi.org/10.1007/s41096-021-00102-5