Under the Hood: How Uber & Lyft have Changed Our Lives

Over the past decade or so, the entry of Uber, Lyft, and other transportation network companies (or TNCs) in nearly every American community has changed how we interact with our transportation ecosystem, whether as passengers, drivers, or other stakeholders. Popular perceptions of the widespread impact of TNCs stem from how we interact with them day-to-day: as a complement to public transit, a part-time work opportunity, a source of increased traffic congestion, or the scourge of road safety.

Like all amenities, cities have a responsibility to strike a balance between the convenience of TNCs and the regulations that keep residents happy, healthy, and safe. This, of course, often takes the form of transportation policies—which, in order to be truly effective, need to be rooted in reliable data and techniques to result in measurable outcomes. However, many cities lack the blueprints (data, data science, or both) in order to inform and craft such policies.

A recently-published series of policy briefs from a team of researchers at Carnegie Mellon University aims to change that. The series summarizes the key findings of several peer-reviewed articles and working papers. Here, we describe those findings through the lens of Lacuna’s three pillars of transportation outcomes: environmental sustainability, social equity, and economic well-being, and analyze how cities might extract value from the learnings therein.

Policy briefs: environmental sustainability

Transportation is, of course, a major contributor to anthropogenic climate change. As TNCs are now a significant part of the transportation system, it is critical to quantify the environmental costs of operating TNCs, and to know how they could be mitigated. A pair of papers in the Journal of Environmental Science and Technology attempt to answer these questions respectively.

The first paper simulates replacing trips of private vehicles with those of TNCs in six US cities. They find that this replacement ends up increasing the overall external costs (due to crashes, congestion, and pollution) by 32-37 cents per trip. Any gains that accrue from fewer vehicle cold starts are more than offset by the costs of the so-called deadheading miles of TNCs between passenger trips.

The second paper is based on the general idea among economists that pricing climate externalities will encourage the private sector to embrace greener technology. It estimates how TNCs might respond to taxes on their external costs, and finds they would be incentivized to electrify more of their fleet. Their savings on air emissions costs would range from 10% in New York City to 22% in Los Angeles.

Policy briefs: social equity

Embedded in the usage patterns of TNCs are several questions regarding equity of access and service. The brief series touches on two specific questions. One working paper measured how TNC ridership changed in Chicago neighborhoods of varying incomes after the onset of COVID-19. It found the drop was larger for higher-income neighborhoods, suggesting that low-income travelers appear to be more dependent on TNCs and did not have as much flexibility to adjust their travel behavior.

Another working paper conducted a similar analysis on New York City neighborhoods in response to heat waves. It found that higher-income neighborhoods increased their TNC usage more than lower-income neighborhoods in response to the heat wave, suggesting that lower-income riders are more vulnerable to extreme outdoor weather while waiting for public transit. Both working papers suggest the need to explicitly adjust transit patterns or provide special service for low-income neighborhoods in order to advance social equity objectives.

Policy briefs: economic well-being

Last but not least, what is the effect of TNCs on the overall economic activity of a city? How can that effect be disentangled from other changes to the city over time?

In an iScience paper, researchers exploit a so-called natural experiment resulting from the staggered launch of TNC services in over 200 urban areas. They found that, in response to the launches, car ownership increased on average—particularly in cities with slowing economic growth and more dependence on cars. A working paper determined that TNCs overall have contributed to economic growth, employment, and wages for intermittent work.

Two other working papers considered the effects of congestion pricing and ride pooling. The first studied a tax that the City of Chicago imposed in Jan 2020 to incentivize pooling and disincentivize traveling downtown and other special zones during times of maximum demand. They estimated the policies had their desired effect, increasing pooling rates by 3% and reducing travel to peak demand areas by 8%. The second found the externalities of congestion, crashes, and emissions were reduced by 18% due to ride pooling — but that most of the motivation for TNCs to pool rides came from their own economic interests; in other words, additional policy interventions might not move the needle much on uptake, though they might be justified for equity considerations.

How cities can harness these lessons, now and in the future

To yield the key findings in these papers, the team at Carnegie Mellon required multiple full-time researchers working for years on several different datasets. This is the gold standard for understanding long-term effects to transportation—but cities generally do not have such resources. Moreover, these papers describe one place at one time and are not necessarily universal; findings in Chicago have limited applicability to Dallas, Tampa, or Seattle.

To create positive, policy-driven change in its own community, each individual city would, in effect, use these experiments as a template, but run them using data that describes the places and times unique to that city. In other words:

Cities need effective, performance-based metrics they can measure in real-time and use to represent their desired outcomes over a range of modes and applications.

Lacuna’s vision of a transportation operating system (tOS) and its underlying foundation of digital infrastructure goes along way in achieving this objective, empowering cities to:

• Collect relevant data in near-real-time and analyze it safely and securely

• Dynamically create, communicate, and adjust digital policies

• Utilize common standards and frameworks that scale to other modes, both current (such as micromobility, taxi, or freight/delivery vehicles) and near-future (such as autonomous vehicles or advanced air mobility)

To learn more, and what a tOS can do for cities today and tomorrow, contact us.