Influencing factors and heterogeneity in ridership of traditional and app-based taxi systems
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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.
KeywordsStreet-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.
- Anselin, L.: Spatial econometrics. In: Baltagi, B. (ed.) A companion to theoretical econometrics, pp. 310–330. Blackwell Publishing Ltd., Malden (2001)Google Scholar
- Bialik, C., Flowers, A., Fishcher-Baum, R., Mehta, D.: Uber is serving New York’s outer boroughs more than taxis are (2015). http://fivethirtyeight.com/features/Uber-is-serving-new-yorks-outer-boroughs-more-than-taxis-are. Accessed 17 July 2018
- Castrodale, J.: San Francisco’s biggest cab company files for bankruptcy—and you can guess why (2016). http://www.usatoday.com/story/travel/roadwarriorvoices/2016/01/10/san-franciscos-biggest-cab-company-files-for-bankruptcy-and-you-can-guess-why/83310568/. Accessed 17 July 2018
- Fischer-Baum, R., Bialik, C.: Uber is taking millions of Manhattan rides away from taxis (2015). http://fivethirtyeight.com/features/Uber-is-taking-millions-of-manhattan-rides-away-from-taxis/. Accessed 17 July 2018
- 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). https://doi.org/10.1016/j.regsciurbeco.2010.01.002 CrossRefGoogle Scholar
- Newsham, J., Adams, D.: Amid fight with Uber, Lyft, Boston taxi ridership plummets. (2015). https://www.bostonglobe.com/business/2015/08/19/boston-taxi-ridership-down-percent-this-year/S9dZMELMye6puzTTYoDIrL/story.html/. Accessed 17 July 2018
- New York City Taxi and Limousine Commission (NYCTLC): 2016 TLC Factbook. New York City: NYCTLC (2016). http://www.nyc.gov/html/tlc/downloads/pdf/2016_tlc_factbook.pdf. Accessed 17 July 2018
- Silver, N., Fischer-Baum, R.: Public transit should be Uber’s new best friend (2015). http://fivethirtyeight.com/features/public-transit-should-be-Ubers-new-best-friend/. Accessed 17 July 2018
- Theis, M.: The Uber effect: Austin taxi rides drop dramatically in past year (2016). http://www.bizjournals.com/austin/news/2016/01/19/the-Uber-effect-austin-taxi-rides-drop.html/. Accessed 17 July 2018
- Washington, S.P., Karlaftis, M.G., Mannering, F.: Statistical and Econometric Methods for Transportation Data Analysis. CRC Press, Boca Raton (2010)Google Scholar
- 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
- 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). https://doi.org/10.1109/BigData.2017.8258070