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Are travelers substituting between transportation network companies (TNC) and public buses? A case study in Pittsburgh


Transportation network companies (TNC) provide mobility services that are influencing travel behavior in unknown ways due to limited TNC trip-level data. How they interact with other modes of transportation can have direct societal impacts, prompting appropriate policy intervention. This paper outlines a method to inform such policies through a data-driven approach that specifically analyzes the interaction between TNCs and bus services in Pittsburgh, PA. Uber surge multiplier data is used over a 6-month time period to approximate TNC usage (i.e., demand over supply ratio) for ten predefined points of interest throughout the city. Bus boarding data near each point of interest is used to relate TNC usage. Data from multiple sources (weather, traffic speed data, bus levels of service) are used to control for conditions that influence bus ridership. We find significant changes in bus boardings during periods of unusually high TNC usage at four locations during the evening hours. The remaining six locations observe no significant change in bus boardings. We find that the presence of a dedicated bus way transit station or a nearby university (or dense commercial zones in general) both influence ad-hoc substitutional behavior between TNCs and public transit. We also find that this behavior varies by location and time of day. This finding is significant and important for targeted policies that improve transportation network efficiency.

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  1. UberHOP is a fixed-route, flat-rate form of micro-transit that was piloted in Seattle, WA during 2015.

  2. Small MSAs include all MSAs with populations less than the median MSA population while large MSAs have populations greater than the median MSA population.

  3. Transit scores were obtained from AllTransit. AllTransit provides transit scores at a city level for all different modes of transit.

  4. The minimum surge multiplier for Uber services is 1.2, which means that the rider fare is multiplied by 1.2, making the fare 20% greater. The minimum surge for Lyft is 25%, meaning that an additional 25% of the fare is charged to the rider.



  7. U.S. Census Bureau, 2015 American Community Survey.


  9. Data is not collected for every time period due to limited probe vehicles.

  10. The thresholds were chosen to match surge multiplier levels used by Uber at the time of the study.


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This research is funded in part by National Science Foundation Award CMMI-1751448 and Carnegie Mellon University’s Mobility21, a National University Transportation Center for Mobility sponsored by the US Department of Transportation. The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. The U.S. Government assumes no liability for the contents or use thereof. We would also like to thank the anonymous reviewers for their valuable suggestions.

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RG: literature review, study conception and design, data analytics, analysis and interpretation of results, manuscript preparation. SQ: study conception and design, data acquisition, analysis and interpretation of results, manuscript preparation. HSM interpretation of results, manuscript preparation. CH interpretation of results, manuscript preparation

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Correspondence to Sean Qian.

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Grahn, R., Qian, S., Matthews, H.S. et al. Are travelers substituting between transportation network companies (TNC) and public buses? A case study in Pittsburgh. Transportation 48, 977–1005 (2021).

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  • Transportation network companies (TNC)
  • Ride hailing
  • Shared mobility
  • Travel behavior
  • Uber
  • Lyft