Do ride sharing services make traffic congestion worse?

11 minute read


The introduction of ride sharing platforms like Uber and Lyft have made our lives significantly better. Gone are the days where not owning a car meant meticulous examination of public transport schedules or expensive cab rides. They have so seamlessly integrated into our lives that it’s hard for me to even think about life without access to a ride on demand, wherever I am, at whatever time of the night.

These platforms have used technology to upend the incumbent taxi industry and have successfully rewritten the rules of urban commuting. Firstly, by not owning any of the cars on their platforms they reduce their operating costs significantly and brand themselves as technology companies. This asset-light model that they use has allowed them to scale rapidly and enter multiple cities in quick succession. Secondly, they take pride in using algorithms that use price as a lever - ‘surge pricing’ in Uber-speak - to efficiently match supply and demand. This ensures that passengers have access to rides in locations or at times that are unfavourable to the drivers.

These multi-side sharing economy platforms have not only transformed the taxi business, but have been making waves in other industries as well. Airbnb is a sharing economy platform that operates in the hospitality space. TaskRabbit matches people willing to perform tasks to people who want these tasks done on demand. Kickstarter, a fundraising platform, matches people looking for funds to execute projects with people willing to back these projects.

The widespread impact that these platforms have had and the scale at which they operate now have made them, increasingly, subject to regulatory scrutiny. This has led to people asking questions about the societal impact of these platforms. With respect to ride sharing, local governments of large urban areas that are plagued by traffic congestion are interested in whether the introduction of these platforms contributes to an increase in traffic congestion.

There are two differing perspectives on this issue; proponents of ride-sharing services argue that the low cost of these services and ease of access will provide a convenient alternative to driving alone and thus reduce traffic congestion and car ownership in the long run. Critics of ride-sharing services, on the other hand, argue that the low cost of ride-sharing services will divert trips from other modes of public transit or non-motorized transport to these services and perhaps, introduce new trips altogether.

Anecdotally, there is city specific evidence that ride sharing platforms do, in fact, contribute to an increase in traffic congestion. However, this is far from conclusive. Anecdotal evidence is often unreliable because it is affected by false causality and the availability bias. For example, if you lived in a city for a period of time, various things that affect traffic congestion could be happening concurrently. If you suffer from the availability bias - most of us do - you might attribute the total effect of all the factors affecting traffic congestion to the introduction of ride sharing, just because it is in the news and people are talking about it. There could, however, be a correlation between the introduction of ride sharing and a rise or decrease in traffic congestion. But correlation does not imply causation.

Econometrics without the equations

The best shot are a rigorous answer to this question of isolating the causal impact of ride sharing platforms is by using data of traffic congestion across various urban areas in the United States cities. This brings us into the realm of experimental design and econometrics.

Breaking this down, it’s helpful to first identify what conditions are needed for causal inference in an contrived experimental setting. The key element in any study that seeks to identify causal effects is random assignment of the treatment and control. For example, in the case where the efficacy of a drug is being tested, for accurate causal inference the drug and the placebo have to be given at random to people. If the drug were given only to people who had a disease and the placebo to another group with different characteristics, it would mean that the actual effect of the drug tested against the control would be skewed. In addition to this, an accurate control group that the counterfactual is built on is also required, this is also taken care of by the random assignment. The assumption that is being made here in the case of the drug is that the control group would react in a similar manner to the treatment in the event that their roles were switched.

In the case of real world examples like estimating the impact of ride sharing on traffic congestion, it’s impossible to construct such experiments. Does this mean that it is impossible to perform causal inference? Fortunately not. What can be exploited to draw causal inference is what is known as a natural experiment, these are naturally occurring events that mimic the characteristics of a contrived experimental setup with random assignment and an accurate control group on which the counterfactual is based. In the case of ride sharing, specifically the entry of the low cost ride sharing product from Uber, Uber X, there is an excellent natural experiment that lends itself to causal inference.

The primary method of exploiting natural experiments to draw causal inference is known as the ‘difference-in-difference’ technique in Econometrics lingo. It rests on the assumption that in the absence of an intervention on the treated group, the treatment group and the control group would behave in the same manner - known commonly as the parallel trends assumption. If this is the case, post the intervention, the causal effect of the treatment is simply the difference between - what would have been in the absence of the treatment, the counterfactual and what happened after the treatment.

The entry of Uber X into various urban centers in America is conveniently varied across time, which gives a natural experiment type setting to investigate the causal impact of Uber on traffic congestion. The data that enables this study comes from various sources; firstly, traffic congestion data is obtained from the Urban Congestion Report (UCR), secondly, the dates that Uber entered various cities are captured from blog posts that Uber puts out before they launch in any new city. Finally, data on other factors that might affect traffic congestion such as population and GDP are obtained from the Bureau of Economic Analysis.

This estimation of the effect of the entry Uber X on traffic congestion is based on a regression design of the ‘difference-in-difference’ estimation technique. The basic idea that underpins this design is identical to what is described above. It requires that the entry of Uber is random with respect to traffic congestion and that the effect of the entry of Uber on traffic congestion is the same across all cities. If these were not met, it would violate the random assignment and accurate control assumptions and not lead to causal inference. The advantage of this design is the fact that other factors that affect traffic congestion can be controlled for, leading to a more accurate estimation.


The rigor of Econometrics requires that results are accepted as robust only if they conform to a 95% level of confidence - that is, the specific results occur only 5% of the time due to random chance. Even with great data and a good Econometric design, this is hard to achieve. Unfortunately, as is the case with many Econometric studies, in this case, it turned out that there was small increase in traffic congestion that could be causally attributed to the introduction of Uber X, but it lacks the statistical significance to make a robust claim.

Potential reasons for these results

  • Lack of Data

The data that this study is based on is limited, in that it doesn’t account for some large cities that could drive the results one way or another. New York, a large urban center with heavy usage of Uber is not represented in this study. Including these results could perhaps, tell a different story or add statistical significance to these results.

  • Time effects, the effect that it takes for consumer behaviour to change is longer

There is a time lag between widespread adoption of a new product of service and the launch of a product or service. The behaviour of people needs to change, less people would likely be using Uber X right when they launched as opposed to a couple of years in. These results could be explained by the fact that the effect of Uber X has not had time to fully manifest itself. The story could very well be different given data over a longer timeframe.

  • Carpooling services - Uber Pool and Lyft Line

A significant limitation of this study is that it considers only the entry of Uber X, a product that matches one rider to one driver. The more interesting case, however, is the concept of carpooling that these services enable. Products like Uber Pool and Lyft Line are where the greatest gains from a reduction of traffic congestion stand point may be, these services group users travelling in the same direction and match one driver to multiple users. These services, however, are new and there isn’t sufficient data post their introduction to run a good difference-in-difference study because of the time effect of the intervention mentioned above.

The Path Ahead

It is clear that irrespective of their impact on traffic congestion, ride sharing services are here to stay. This fact alone warrants further investigation into its societal impact in a data driven manner. Unfortunately, in the case of ride sharing the most valuable data is with the ride sharing companies themselves. This data has information on a fine granular level that could unearth the answers about the effect of ride sharing on traffic congestion and other societal issues. Local governments have begun requesting this data - it remains to be seen what the analysis of this data points to. In addition to this, adding more controls to the difference-in-difference design could produce better results, for example, the intensity of public transportation in a given urban area, if available, will make a very good control.

On a final, albeit tangential, note. We are currently in the initial phases of a revolution in transportation, self-driving cars are going to be a reality much sooner than most of us imagine. Ride sharing companies are investing heavily in this technology, the real gains from algorithmically managed ride sharing services might actually be in the future when self driving cars are a reality and commonly used. The widespread adoption of this technology could lead to efficiency gains through efficient allocation that alleviates traffic congestion in most urban areas. If this becomes a reality, it could be that app based ride sharing, while currently contributing to traffic congestion could eventually be the solution to congested cities.

Here is a link to the actual paper.