Ph.D. Dissertation

Impacts of Ridesourcing – Lyft and Uber – on Transportation including

VMT, Mode Replacement, Parking, and Travel Behavior

Abstract: Ridesourcing or transportation networking companies (TNCs) such as Uber and Lyft, are changing the way in which people perceive travel; and is the primary window through which we can estimate how society will react when Mobility as a Service (MaaS) is delivered at a larger scale via fleets of automated vehicles. The challenge is the limited data and research available to understand the impacts of ridesourcing on travel patterns and their related transportation implications. Understanding travel behavior impacts, congestion issues, and mobility system efficiency of ridesourcing is critical in planning infrastructure and policy for the future. This doctoral dissertation analyzes the impacts of ridesourcing on several areas of transportation including: driver efficiency in terms of vehicle in-use with and without a passenger, system efficiency in terms of distance – Vehicles Miles Traveled (VMT) versus Passenger Miles Traveled (PMT) – using before (mode replacement) and after scenarios, VMT changes, parking, and travel behavior. Realizing the difficulty in obtaining data directly from Lyft and Uber, this research employed an anthropological and survey-based approach, to gain access to data and new real-time driver- and passenger- oriented perspectives, by conducting research while driving for both companies. The new datasets offer the ability to explore travel attributes from 416 rides (Lyft, UberX, LyftLine, and UberPool), as well as travel behavior and demographics from 311 passengers. The results are divided in four main parts: driver dataset, VMT, parking, and travel behavior.


Driver Dataset: Only 39.3% (based on time) and 59.2% (based on distance) of vehicle in-use was with a passenger. Accounting expenses, ridesourcing average earnings was around $8 per hour.

The overall driver efficiency rate for this study, accounting for commute time at the end of the shift, is 39.3% based on time, and 59.2% based on distance. Driver efficiency or ‘vehicle in-use with a passenger' could be even lower for other drivers since, by research design, the driver minimized cruising for a ride request and did not accept rides requiring long travel. The average earnings (n=416 trips) was $15.69/hour, but this does not include expenses. Calculating driving expenses to be between $0.28 per mile to the U.S. Federal Standard 2016 mileage rate of $0.54 per mile, ridesourcing drivers make, in reality, between $5.38 and $10.36 per hour, with an average of $7.94/hr (including tips, for the case of Lyft; as Uber does not offer the tip option).


PMT vs. VMT: Results indicate induced travel (12% of TNC passengers would not have traveled otherwise); significantly less system efficiency as the PMT/VMT ratio went from 1.1 before ridesourcing to 0.6 after ridesourcing; and increased in VMT to 185%.

Ridesourcing provides more mobility, 12.2% passengers “wouldn’t have traveled” (Figure 1), but affects the efficiency of transporting passenger versus vehicles going from a PMT/VMT efficiency of 112.3% to 60.8%. [Note: PMT offers a metric to more readily measure inefficiencies by taking into account passengers and not just vehicles such as single versus high occupancy vehicles]. The results from this study also show that VMT increased to 185% for ridesourcing passengers using the VMT baseline of riders travel behavior without these services (Table 1), which has significant implications for our cities in terms of congestion and environmental concerns. If the results for the datasets collected in Denver, CO, are representative for the entire country, the VMT impact of ridesourcing would be around 5.5 billion extra miles per year in the U.S. [Note: The study also looks into larger behavioral shifts such as car ownership and general mode share changes but for this study only specific TNC trip data was used].

Figure 1. Mode Replacement

Figure 1. Mode Replacement

Table 1. VMT by Mode Replaced, before and after

Parking: Ridesourcing allows parking supply to decrease.

When driving trips (e.g. SOV, carpool drive, car rental) are being replaced, the need for parking is reduced. At the same time, parking was the second top reason – after avoiding drinking and driving – as to why passengers with high drive frequency use ridesourcing instead of driving. Continuing with this cycle of reducing parking supply will help decrease car dependency. Additional research such as parking rates at airports and crashes statistics in and around popular attractions areas should be able to corroborate such findings at larger scales.


Travel Behavior: Based on modality style, four groups of ridesoucing passengers were identified: typical drivers, multimodals, non-drivers, and bi-modal style based on trip purpose.

Typical drivers are those passengers that mostly travel by driving their own car, multimodals travel by several modes of transportation (including driving), non-drivers do not have a driving component in their travel options, and bi-modal style passengers are those who adopt different travel behavior based on specific trip characteristics such as trip purpose. For passengers that are typical drivers, ridesourcing mostly replaced social trips (e.g. go out), to/from airport, and when out of town. For typical multimodals or non-drivers, ridesourcing replaced work and school trips with the main reason being that public transportation is not available.


This dissertation indicates results that pose the following risks of increased uptake in ridesourcing:  PMT/VMT efficiency rates drops with ridesourcing (and only better than “taxis” and “get a ride” modes), potential for replacing more sustainable modes (e.g. transit, biking, walking), and overall increase in VMT. Other results indicate potential for decreases in car ownership, parking demand, and opportunities to change land use. These results give us insight into the potential risks and benefits of ridesourcing on several key aspects of transportation. This, in turn, will help cities and transportation organizations better account for and shape technology, infrastructure, planning, and policy approaches for ridesourcing that helps maximize mobility and minimize energy consumption.


  • Full Doctoral Dissertation: Open Access Link

  • Doctoral Oral Defense: January 19, 2017. Full Presentation Link

News & Press:

Colorado Public Radio (CPR), May 2017. “Two Ways Uber And Lyft Are Clogging Denver-Area Streets, And One Way They’re Helping”


Denver7 News, April 2017. “Study: Uber and Lyft could be adding traffic to Colorado roads”


Streetsblog Denver, March 2017. “Study: Uber and Lyft Add Traffic, Reduce Efficiency on Denver and Boulder Roads”

Book Chapter:

Henao, Alejandro and Wesley E. Marshall (2017). A Framework for Understanding the Impacts of Ridesourcing on Transportation. In Disrupting Mobility: Impacts of Sharing Economy and Innovative Transportation on Cities (pp. 197-209), Meyer & Shaheen (Eds.). Springer International Publishing.

Working Papers:

  • Henao, A., & Marshall, W. E. (2017). Impacts of Ridesourcing – Lyft and Uber – on Mode Replacement, Transportation Efficiency, and Vehicle Miles Traveled: A Case Study in Denver, Colorado (submitted)

  • Henao, A., & Marshall, W. E. (2017). Do Uber and Lyft Drivers Even Make Minimum Wage? (submitted)

  • Henao, A., & Marshall, W. E. (2017). Impacts of Ridesourcing – Lyft and Uber – on Parking

  • Henao, A., & Marshall, W. E. (2017). Benefits and Risks of Ridesourcing: What about Transportation Policy?

  • Henao, A., & Marshall, W. E. (2017). Impacts of Ridesourcing – Lyft and Uber – on Travel Behavior


Presentations on Ridesourcing:

  • Presentation to the Colorado Department of Public Health & Environment,  Denver, CO. August 2017.

  • Automated Vehicle Symposium, San Francisco, California. July 2017.

  • ITE Western District Meeting, San Diego, California. June 2017.

  • Designing Innovative Transportation Systems Solutions Workshop, Berkeley, California. May 2017.

  • 14th Annual Spring Transportation Symposium, Denver, CO. April 2017.

  • Time Use Observatory (TUO), Chile. March 2017

  • Grupo Sur, Universidad de Los Andes, Bogota, Colombia. March 2017. Conference in Spanish.

  • Presentation to the City and County of Denver.  Denver, CO. February 2017.

  • 96th Annual Meeting of the Transportation Research Board, Washington, D.C. January 2017.

    • Event 105: Innovative Doctoral Research from Dwight Eisenhower Transportation Fellowship, Lectern Session (P17-20637)

    • Event 195: Doctoral Research in Transport Modeling, Poster Session (P17-21556). Link

    • Event 665: New Mobility Services: New Research Evidence, Poster Session (P17-06830). Link

  • ITE Western District Meeting, Albuquerque, NM. July 2016.

  • 13th Annual Spring Transportation Symposium, Denver, CO. April 2016.

  • Disrupting Mobility Summit, MIT, Cambridge, MA. November 2015.

2018 by Alejandro Henao