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Spatiotemporal Analysis of Ridesourcing and Taxi Usage by Zones


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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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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Correspondence to Nalini Ravishanker.

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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).

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  • Moran’s I
  • Ridesourcing
  • Spatial clustering
  • Taxi zones in NYC
  • Temporal dependence