Influencing factors and heterogeneity in ridership of traditional and app-based taxi systems

  • Wenbo Zhang
  • Tho V. Le
  • Satish V. UkkusuriEmail author
  • Ruimin Li


The growth of app-based taxi services has disrupted the urban taxi market. It has seen significant demand shift between the traditional and emerging app-based taxi services. This study explores the influencing factors for determining the ridership distribution of taxi services. Considering the spatial, temporal, and modal heterogeneity, we propose a mixture modeling structure of spatial lag and simultaneous equation model. A case study is designed with 6-month trip records of two traditional taxi services and one app-based taxi service in New York City. The case study provides insights on not only the influencing factors for taxi daily ridership but also the appropriate settings for model estimation. In specific, the hypothesis testing demonstrates a method for determining the spatial weight matrix, estimation strategies for heterogeneous spatial and temporal units, and the minimum sample size required for reliable parameter estimates. Moreover, the study identifies that daily ridership is mainly influenced by number of employees, vehicle ownership, density of developed area, density of transit stations, density of parking space, bike-rack density, day of the week, and gasoline price. The empirical analyses are expected to be useful not only for researchers while developing and estimating models of taxi ridership but also for policy makers while understanding interactions between the traditional and emerging app-based taxi services.


Street-hailing taxi App-based taxi Heterogeneity Spatial lag Simultaneous equation model 



The authors acknowledge the Uber trip data by FiveThirtyEight and yellow and Boro taxi trip data by New York City Taxi and Limousine Commission.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Anselin, L.: Spatial Econometrics: Methods and Models. Kluwer Academic, Boston (1988)CrossRefGoogle Scholar
  2. Anselin, L.: Spatial econometrics. In: Baltagi, B. (ed.) A companion to theoretical econometrics, pp. 310–330. Blackwell Publishing Ltd., Malden (2001)Google Scholar
  3. Bialik, C., Flowers, A., Fishcher-Baum, R., Mehta, D.: Uber is serving New York’s outer boroughs more than taxis are (2015). Accessed 17 July 2018
  4. Castrodale, J.: San Francisco’s biggest cab company files for bankruptcy—and you can guess why (2016). Accessed 17 July 2018
  5. Chen, D.T., Wang, Y., Kockelman, K.M.: Where are the electric vehicles? a spatial model for vehicle-choice count data. J. Transp. Geogr. 43, 181–188 (2015). CrossRefGoogle Scholar
  6. Dias, F.F., Lavieri, P.S., Garikapati, V.M., Astroza, S., Pendyala, R.M., Bhat, C.R.: A behavioral choice model of the use of car sharing and ride-sourcing services. Transportation (2017). CrossRefGoogle Scholar
  7. Fischer-Baum, R., Bialik, C.: Uber is taking millions of Manhattan rides away from taxis (2015). Accessed 17 July 2018
  8. Jager, B., Wittmann, M., Lienkamp, M.: Analyzing and modeling a city’s spatiotemporal taxi supply and demand: a case study for Munich. J Traffic Logist Eng 4(2), 147–153 (2016). CrossRefGoogle Scholar
  9. Jeanty, P.W., Partridge, M., Irwin, E.: Estimation of a spatial simultaneous equation model of population migration and housing price dynamics. Reg Sci Urban Econ 40, 343–352 (2010). CrossRefGoogle Scholar
  10. Kamga, C., Yazici, M.A., Singhal, A.: Analysis of taxi demand and supply in New York City: implications of recent taxi regulations. Transp Plan Technol 38(6), 601–625 (2015). CrossRefGoogle Scholar
  11. Lavieri, P.S., Dias, F.F., Juri, N.R., Kuhr, J., Bhat, C.R.: A model of ridersourcing demand generation and distribution. J Transp Res Board Transp Res Record (2018). CrossRefGoogle Scholar
  12. Li, H., Calder, C.A., Cressie, N.: Beyond Moran’s I: testing for spatial dependence based on the spatial autoregressive model. Geogr. Anal. 39(4), 357–375 (2007). CrossRefGoogle Scholar
  13. Liu, Y., Wang, F., Xiao, Y., Gao, S.: Urban land uses and traffic ‘source-sink areas’: evidence from GPS-enabled taxi data in Shangahi. Landsc Urban Plan 106(1), 73–87 (2012). CrossRefGoogle Scholar
  14. Narayanamoorthy, S., Paleti, R., Bhat, C.R.: On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level. Transp Res Part B Methodol 55, 245–264 (2013). CrossRefGoogle Scholar
  15. Neoh, J.G., Chipulu, M., Marshall, A.: What encourages people to carpool? an evaluation of factors with meta-analysis. Transportation 44, 423–447 (2017). CrossRefGoogle Scholar
  16. Newsham, J., Adams, D.: Amid fight with Uber, Lyft, Boston taxi ridership plummets. (2015). Accessed 17 July 2018
  17. New York City Taxi and Limousine Commission (NYCTLC): 2016 TLC Factbook. New York City: NYCTLC (2016). Accessed 17 July 2018
  18. Qian, X., Ukkusuri, S.V.: Spatial variation of the urban taxi ridership using GPS data. Appl. Geogr. 59, 31–42 (2015). CrossRefGoogle Scholar
  19. Qian, X., Zhan, X., Ukkusuri, S.V.: Characterizing urban dynamics using large-scale taxicab data. In: Lagaros, N.D., Papadrakakis, M. (eds.) Engineering and Applied Sciences Optimization, pp. 17–32. Springer, Basel (2015)CrossRefGoogle Scholar
  20. Qian, X., Zhang, W., Ukkusuri, S.V., Yang, C.: Optimal assignment and incentive design in the taxi group ride problem. Transp. Res. Methodol. 103, 208–226 (2017). CrossRefGoogle Scholar
  21. Shaheen, S.A., Chan, N.D., Gaynor, T.: Casual carpooling in the San Francisco Bay Area: understanding user characteristics, behaviors, and motivations. Transp. Policy 51, 165–173 (2016). CrossRefGoogle Scholar
  22. Silver, N., Fischer-Baum, R.: Public transit should be Uber’s new best friend (2015). Accessed 17 July 2018
  23. Stakhovych, S., Bijmolt, T.H.A.: Specification of spatial models: a simulation study on weights matrices. Pap. Reg. Sci. 88(2), 389–408 (2008). CrossRefGoogle Scholar
  24. Theis, M.: The Uber effect: Austin taxi rides drop dramatically in past year (2016). Accessed 17 July 2018
  25. Washington, S.P., Karlaftis, M.G., Mannering, F.: Statistical and Econometric Methods for Transportation Data Analysis. CRC Press, Boca Raton (2010)Google Scholar
  26. Yang, C., Gonzales, E.: Modeling taxi trip demand by time of day in New York City. Transp. Res. Record J. Transp. Res. Board 2429, 110–120 (2014). CrossRefGoogle Scholar
  27. Yang, K., Lee, L.: Identification and QML estimation of multivariate and simultaneous equations spatial autoregressive models. J. Econom. 196, 196–214 (2017). CrossRefGoogle Scholar
  28. Zhang, W., Ukkusuri, S.V.: Optimal fleet size and fare setting in emerging taxi markets with stochastic demand. Comput.-Aided Civ. Infrastruct. Eng. 31(9), 647–660 (2016). CrossRefGoogle Scholar
  29. Zhang, F., Zhu, X., Guo, W., Ye, X., Hu, T., Huang, L.: Analyzing urban human mobility patterns through a thematic model at a finer scale. ISPRS Int. J. Geo Inf. 5(6), 78 (2016a). CrossRefGoogle Scholar
  30. Zhang, W., Qian, X., Ukkusuri, S.V.: Identifying the temporal characteristics of intra-city movement using taxi geo-location data. In: Konomi, S., Roussos, G. (eds.) Enriching Urban Spaces with Ambient Computing, the Internet of Things, and Smart City Design, pp. 68–88. IGI Global, Pennsylvania (2016b)Google Scholar
  31. Zhang, W., Ukkusuri, S.V., Lu, J.J.: Impacts of urban built environment on empty taxi trips using limited geolocation data. Transportation 44(6), 1445–1473 (2017a). CrossRefGoogle Scholar
  32. Zhang, W., Kumar, D., Ukkusuri, S.V.: Exploring the dynamics of surge pricing in mobility-on-demand taxi services. In: Proceedings of 2017 IEEE International Conference on Big Data (2017b).
  33. Zhang, W., Ukkusuri, S.V., Yang, C.: Modeling the taxi drivers’ customer-searching behaviors outside downtown areas. Sustainability 10(9), 1–23 (2018). CrossRefGoogle Scholar
  34. Zhou, B.B., Kockelman, K.M.: Predicting the distribution of households and employment: a seemingly unrelated regression model with two spatial processes. J. Transp. Geogr. 17(5), 369–376 (2009). CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Wenbo Zhang
    • 1
  • Tho V. Le
    • 1
  • Satish V. Ukkusuri
    • 1
    Email author
  • Ruimin Li
    • 2
  1. 1.Lyles School of Civil EngineeringPurdue UniversityWest LafayetteUSA
  2. 2.Department of Civil EngineeringTsinghua UniversityBeijingChina

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